Wind energy is one of the most attractive renewable energy sources because of its low operating, maintenance, and production costs as well as its low environmental impact. The goal of this study is to discover the best locations in Bangladesh where wind farms can be built and operated efficiently. This study applied the GIS and AHP methodologies to examine the eight important parameters upon which the suitability of locations is highly dependent. This analysis finds that Bangladesh has large regions appropriate for wind farm installation, with 3718.76 km² and 16631.14 km² classified as "very high" and "high" suitability, respectively. It was also observed that wind speed, land slope, and elevation each had a height-weighted criterion of 32 %, 27 %, and 12 %, respectively, when picking suitable locations. However, the viability of this study in identifying suitable sites has been evaluated based on ROC and AUC techniques and found satisfactory as per AUC value. The knowledge gained from this study will help the sustainable and renewable energy development authority (SREDA), Bangladesh to expedite the renewable energy investment process and will ensure the great certainty. The findings of this research can be considered as baseline information in the wind energy sector.

Urban wind energy has emerged as an attractive source of distributed generation in cities to achieve sustainable development goals. The advance in technologies for the use of urban wind energy has turned this source into an attractive alternative for the decarbonization of cities and the energy transition. The objectives of this work are (1) to identify the potential of wind energy through numerical weather prediction (NWP) data tools and (2) to identify the roles and responsibilities of the stakeholders involved in the decision-making process. A methodology was developed in two phases and applied to a case study in the Dominican Republic. The first phase consisted of estimating the wind energy potential for the 32 provinces at a height of 10 m using open-access geographic reference systems tools provided by NASA. In the second phase, 28 stakeholders were identified through snowball sampling. The Responsible, Accountable, Consult, and Informed (RACI) matrix tool was applied to identify the roles of the 28 institutions addressed at the country level as relevant in the decision-making process for the energy sector. The annual average wind speed and energy potential for each province were determined. It was found 24 provinces have poor potential, below <4.5 m/s. In the northwest and east is where there is the greatest potential, between 4.83 - 6.63 m/s. The population density was established and was observed that the provinces with greater potential are less densely populated. Through 59 interviews 28 institutions were identified and evaluated due to their relevance in decision-making for the implementation of energy projects. The MEM has strongly categorized as “A”, electricity distribution companies as “R”, energy associations and universities as “C”, and educational and justice institutions as “I”.

Background: We studied Riveaux Road Fire, which was ignited by multiple lightning strikes in January 2019 and burnt more than 637.19 km² in southern Tasmania, Australia. Aims: We focused on fire weather, such as identification of dynamic wind and vegetation type, in a valley of the study area. Methods: We employed two methods: numerical weather model vertical sounding (NWMVS) and the use of a fire simulator, to quantify and examine the contribution of dynamic winds to fire behaviour. The NWMVSs allow rapid diagnosis of changes in wind, temperature, dew point temperature and cloud coverage. Prototype 2 is a fire simulator based on the specification of Australian Fire Danger Rating System (AFDRS). Key results: We found fires to be guided by terrain-forced channelling primarily and by downslope wind conditionally in the valleys. In addition, the fire intensity periodically changed with the magnitude of surface wind, in buttongrass moorland, in which the fire often smoulders, during the fire period according to the satellite image. Conclusions and Implications: Therefore, there should be caution for not only terrain and dynamic wind but also vegetation type during fire spread in rugged terrain.

This work utilizes the particle swarm optimization (PSO) for the optimal sizing of a solar – wind – battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two – step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained is then compared with results from the NGSA-II and hybrid GA – PSO optimization algorithm. An energy management system monitors the energy balance and ensures the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consist of solar panels (154), wind turbine (3), inverter (136) and batteries (29). This results in a minimized TEC, TAC and LCOE of 476, 731USD, 301, 947USD and 0.011USD/kWh respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.

In recent decades the Mediterranean area observed an increase in large fires, despite the decrease in the number of fire ignitions, following a worldwide tendency. Climate change has been the main culprit for large burnt areas, and alterations of forest ecosystems, resulting in the increased recurrence of forest fires. In Portugal, the analogous trend resulted in the destruction of large rural, urban and industrial areas, where after the 2017 months of June and October catastrophic events, the government implemented the 2019 specific policies for preventing fire outbreaks and defending the forest against fires. This compulsory policy demanded that landowners and local governments alike needed to contact specific authorities requesting authorization for their intentions to clean properties in and around urban areas, roads, and forest limits. Despite policy and careful monitoring, ignitions still occur, and in many cases resulting from the implementation of such policies. The study analyses the before and after policy implementation, considering 6904 ignitions records from the period between 2014 and 2023 from the central area of Portugal, covering the transition area between the agricultural fields of the Tagus valley and the Monte-junto-Estrela Mountain range, the district of Santarém. The authorization to use fire in clearance practices is granted according to a set of weather conditions, temperature, wind intensity, and relative humidity, when analyzing fire ignitions with the average daily wind intensity at the site of the occurrence, we observed that more than half of wildfires occur on days with winds between 10 and 20 Km/h, whereas intensities greater than 30 Km/h only 1% of the total occurrences, and of these 22% occurred due to the use of fire in the burning of agricultural leftovers and cut and piled bushes. The circulation of the thermal depression in the Iberian Peninsula gives rise to increasing intensity throughout the day, reaching a maximum in the late afternoon, this factor combined with the misjudgment of safe conditions leads us to suggest stricter intervals and the need for awareness of daily variation in conditions.

Hong Kong was under the direct hit of Severe Typhoon Koinu (2314) on 8 and 9 October 2023, necessitating the issuance of the Increasing Gale or Storm Signal, No. 9. Koinu was a very challenging case for TC forecasting and warning services due to its compact size and erratic movement over the northern part of the South China Sea. This paper reviews the difficulties and challenges on the forecasting aspect of the severe typhoon. The predicted tropical cyclone track and intensity from both conventional models and emerging artificial intelligence models are examined, as well as local wind and rainfall forecast. Experience in this case study showed that while deterministic global models only performed moderately and were not able to adequately support early warning, a regional model and AI models could more effectively support decision making for operational tropical cyclone warning service.

Hong Kong was under strike from Super Typhoon Saola (2309), necessitating the issuance of the highest tropical cyclone warning signal. Saola skirted past the south-southwest of Hong Kong, bringing hurricane force winds and significant storm surge. Saola had its closest approach to Hong Kong on 1 September 2023, posing a unique challenge in forecasting and early warning for the commencement date of the new school term, where higher impact to traffic and public safety was anticipated. This paper covers the challenges on the forecasting aspect of the super typhoon. The predicted tropical cyclone track, intensity and wind structure are reviewed. Experience in this case showed that while there was not a perfect numerical weather prediction model in terms of the forecast track, intensity and wind structure of Saola, multi-model approach provided very use-ful and crucial information for operational weather warning services.

In the present work, the main methodologies used to reconstruct wind fields in the U-SPACE have been analyzed. The SESAR U-SPACE program aims to develop an Unmanned Traffic Management system with a progressive introduction of procedures and services designed to support a secure access to the air space for a large number of drones. The Italian Aerospace Research Center (CIRA) is carrying out the EDUS project focused on the development and validation of operating platform demonstrators serving the micro-scale weather forecasts and the collection of information necessary for the definition of the flight plan of the drones in urban contexts. For this reason, the state of art methodologies that can be used to estimate winds at low altitudes in urban areas starting from available observational data have been reviewed in the present paper. Some of these techniques were originally developed for reconstruction at high altitudes, but successively adapted to treat different heights. A common approach to all techniques is to approximate the probabilistic distribution of wind speed over time with some parametric models, apply spatial interpolation to the parameters and then read the predicted value.

An economic and business history approach is used to show the rise and relative failure of the Spanish wind industry during the period between 2004-2015, when Spain became the fourth country after China, the US and Germany in installed capacity of renewable energies and, in relative terms, the second country after Denmark. This study is unique in that it provides an integrated vision of the reasons for the relative fall of Spain in the world ranking of wind energy producers. The methodology of the economic analysis of industrial policies makes it possible to explain the fall in the relative importance of Spain in the international panorama of wind farms. There were no reasons related to technological obsolescence or inability of the CECRE managing renewable energies to explain the fall.

On 2013 the Villonaco wind farm (16.5 MW), the first wind farm in continental Ecuador near the city of Loja, began operations. The power generated is delivered to the National Interconnected System (SNI), which services the city. This research confronts two sets of real data, the electricity use of the urban area of Loja, and the power generated by the Villonaco Wind Farm. Electricity use follows clearly defined daily and weekly cycles, and wind power has a seasonal behaviour. The study shows that wind power integration cannot be a long-term stable power source regardless power or generation surplus. Another essential finding is that time series can be used as a statistical source to determine the need for short- (seconds) and long- (days, weeks) term energy storage. Strategies to further the energy autonomy of the urban area through the expansion of the wind farm by a factor of 2 are discussed.

The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 meters) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.

Vertical wind speed extrapolation is a commonly used method that involves estimating wind speeds at various heights based on measurements taken at a specific height. In this study, we compared the short-and long-term extrapolation methods of wind parameters using data collected on site by anemometers at two different heights. We assessed the accuracy of these methods by calculating the statistical indicators for performance evaluation, and then determined the most appropriate model for the two types of vertical extrapolation. Additionally, we evaluated energy potential characteristics, as well as the statistical analysis of wind speed distribution and wind power density based on the Weibull and Rayleigh distribution. Our results indicated that the power law is the most suitable model for the short-term method, while the modified Mikhail model is the most appropriate for long-term extrapolation. We also found that the wind potential of this area is favorable for wind turbine integration.

The tension cable-supported power transmission structure (TC-PTS) is a new type of power transmission structure suitable for mountainous terrain, which is sensitive to wind load. In this regard, a nonlinear finite element analysis model of wind-induced vibration is proposed for the TC-PTS, and the wind-induced vibration response of the structure is analyzed. Firstly, the tangent stiffness matrix of the three-dimensional truss element for the supporting suspension cable and transmission line, considering the geometric nonlinearity of structures, is derived through the relationship between the element elastic energy and its displacement. Subsequently, the element mass matrix and damping matrix of the supporting suspension cable and transmission line, as well as the element nodal load vector obtained from wind load equivalence are given. Then, based on the nonlinear finite element theory, the nonlinear dynamic equation of wind-induced vibration is established for the TC-PTS and solved by Newmark-β method combined with Newton-Raphson iterative method. Furthermore, the rain-flow counting method and Miner's linear fatigue cumulative damage theory were used for wind induced fatigue damage assessment. Finally, a two-span TC-PTS is selected as an example, and the wind-induced nonlinear vibration and fatigue damage assessment are analyzed through the proposed model. The results show that the proposed model has high computational accuracy and efficiency. With the increase of wind speed and wind direction angle, the maximum lateral displacement and tension of the supporting suspension cable and transmission line increase, and their degree of increase shows a nonlinear trend. In terms of the wind-induced fatigue analysis results of TC-PTS, the fatigue damage at the end of the supporting-conductor suspension cable is greater than the fatigue damage at its midpoint. Compared to the fatigue damage at the midpoint of the conductor, the fatigue damage at the end of the conductor is less affected by wind direction angle, and both are more significantly affected by the wind speed.

Floating Offshore Wind Turbine (FOWT) is an innovative technology with little industry guidance for its hull design. Various FOWT floaters with different hull shapes claim to support the same turbines. Structural integrity and material expense analyses of different pontoon shapes were conducted, and it was found that some configurations, such as those with every two columns connected by both pontoon and bracing, have advantages over others. However, it is important to note that the choice of pontoon shape should be based on the wave loading conditions the floater will be exposed to. While a T-shaped pontoon provides a cost-effective solution under certain wave loading scenarios, it may not be the best option for all conditions. Specifically, ring pontoon designs with full bracing were found to be necessary for withstanding certain wave loads. Therefore, it is important to consider different Dominant Load Parameters (DLP) and ensure that a FOWT floater can withstand all applicable DLPs. An uneven hexahedral column shape, which combines the best attributes of square and round shapes, is proposed as a better alternative to cylindrical columns. It offers ease of manufacture and reasonably low drag. Bracing is found to be necessary for withstanding the wind turbine’s incurred moment and forces. The conclusion is that platform design should prioritize manufacturing costs and strength over maximizing hydrodynamic performance.

In the context of energy decarbonization, wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The Low Voltage Ride Through (LVRT) characteristic implemented in type IV wind turbine inverters makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1-2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rise in the load for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. The paper presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, or upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes.

Purpose – Extend the Blade Element Momentum Theory (BEMT) such that rotors with pronounced cone and axis angle (tilt or yaw) can be calculated. Derive an equation for the speed ratio (lambda) as a function of Tip Speed Ratio (TSR), radius, blade, cone and axis angle. This converts the BEMT into an Unsteady BEMT or UBEMT. Present the Wagner rotor as one such rotor geometry. --- Methodology – Literature review and calculations. --- Findings – The UBEMT can be used to calculate highly unconventional rotor geometries. --- Research Limitations – Although the aerodynamic coefficients used in the UBEMT are from measurements in steady flow conditions, they can be used with success. --- Practical Implications – Also conventional Horizontal Axis Wind Turbines (HAWT) with noticeable cone and axis angle should be calculated with the UBEMT. The accuracy of power calculations of these HAWTs can be slightly improved. --- Originality – Analytic equations for rotors with cone and axis angle have barely been discussed.

Full-scale/model test comparison studies to validate the traditional ABL wind tunnel simulation technique are reviewed. According to the literature review, notable discrepancies between full-scale measurement results and model test results were observed by most performed comparison studies, but the causes of the observed discrepancies were not revealed in a scientific way by those studies. In this regard, a new research scheme for future full-scale/model test comparison studies is proposed in this article, which utilizes the multiple-fan actively controlled wind tunnel simulation technique. With the new research scheme, future full-scale/model test comparison studies are expected to reasonably disclose the main problems with the traditional ABL wind tunnel simulation technique, and the technique can be improved correspondingly.

Installation of offshore wind farms is rising, driven by the goal of changing the global energy matrix. However, many of its possible impacts are still unknown. Increased noise levels, disruptions to food chains, pollution due to traffic, and impacts on fishing communities and tourism are all potential effects to consider. Marine habitats are essential carbon dioxide sinks. Therefore, losing marine biodiversity due to offshore wind farms can be counterproductive in mitigating climate change. Balancing biodiversity conservation, wind potential, and political interests is challenging. Today, Uruguay has practically decarbonized its electrical matrix, incorporating wind, solar, and biomass energy alongside hydroelectricity. In line with this, the country's Hydrogen Roadmap highlights green hydrogen as relevant, potentially serving as a transportation fuel for both domestic consumption and export. Combining the country's strong base of wind experience with its sustainable policy, it plans to implement offshore wind farms to produce green hydrogen, making studies of its impacts crucial. This paper reviews the current social and environmental information on the Uruguayan coastal habitat, analyzes onshore wind farms' ecological studies, and examines offshore wind farms' global environmental and social impacts. Finally, it proposes suggested studies for environmental approval for offshore wind farms.

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed and implemented product in this study serves as a proof of concept that could be replicated by WMO Members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for input selection based on large-scale indicators to enhance Artificial Intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction of wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead.

The energy transition to wind and solar opens opportunities for green hydrogen as the wind and solar generation tends to bring electricity prices down to very low levels. This paper evaluates whether green hydrogen can integrate well with wind and solar PV to help with improved management of the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during surplus renewable energy periods. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used systems engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, but without those changes, our modelling suggested it would be financially viable within this decade

In the present work, a methodology for wind field reconstruction based on the Meteo Particle Model (MPM) from Numerical Weather Prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP Limited Area Models (LAMs) are run at spatial resolution of about 1 km, this level of information is not sufficient for many applications, for example to support drone operation in urban contexts. The coupling of MPM with the NWP Limited Area Model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values in the generical point considered for the investigation. The numerical results obtained reveal a good behavior of the method in reproducing the general trend of wind speed, as confirmed also by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of spatial and temporal resolution even if the MPM inherits the bias that inevitably affects the COSMO output.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

Extensive surface and upper air measurements of a typhoon over the northern part of the South China Sea, namely, Typhoon Talim in July 2023, are documented and analyzed in this paper. A number of features have been observed from the upper air measurements. First, the log law and the power law are found to be appropriate in fitting the wind profiles of the typhoon in the first 1000 m or so above the sea surface. A low level jet is observed in the lower troposphere from the observations of the radar wind profilers. The paper is also novel from the perspectives that the vertical wind profile from a Doppler LIDAR on an offshore platform over the northern part of the South China sea, and that ocean radar data are used to analyze the surface wind observations of a typhoon in the region. The results of this paper would be useful in understanding the structure of tropical cyclones, e.g. in wind engineering applications.

Currently, there are different types of technology for the production of electricity that use various energy sources, this causes the establishment of generation centers that provide from a small amount to tens of megawatts of electrical energy. These centers are built to supply electricity to nearby loads through networks integrated into a large electrical system or in isolation with their electrical system. The technological evolution, the different energy sources, and the generation centers close to the consumers entail what has been called a distributed generation (DG), the DG carries with it aspects that need to be analyzed. In this paper, the impact of wind generation on the medium voltage energy distribution network is studied, it was determined that the addition to the distribution network of power by wind turbines less than the transmission center does not produce an impact on the network. Simulated results obtained using SIMULINK and DIgSILENT are presented and discussed.

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

SStatistical characteristics of the wind speed in Samaria region of Israel have been analyzed by processing 11 years of wind data provided by the Israeli Meteorological Service, recorded at 10 m height above the ground. The cumulative mean wind speed at measurement height was shown to be 4.53 m/s with standard deviation of 2.32 m/s. Prevailing wind direction is shown to be char-acterized by cumulative mean azimuth of 226° with standard deviation of 79.76°. The results were extrapolated to 70-meter height in order to estimate wind characteristics at hub height of a me-dium-scale wind turbine. Moreover, Weibull distribution parameters were calculated annually, monthly and seasonally, demonstrating a good match with histogram-based statistical repre-sentations. Shape parameter of the Weibull distribution was shown to reside within a narrow range of 1.93 to 2.15, allowing us to assume a Rayleigh distribution, thus simplifying wind tur-bines energy yield calculations. The novelty of the current paper is related to gathering wind statistics for a certain area (Samaria) we are not aware of any published statistics regarding wind velocity and direction in this area. The data may be interesting for potential regional wind energy development in which the obtained Weibull distribution can be used in calculations of expected power generation of particular turbines with known power dependence on velocity. We also point out that the fact that realistic wind velocity statistics is well described by an analytic formula (Weibull distribution) is not trivial, and in fact the fit may have been poor.

This study examines the effect of surface currents on the bulk algorithm calculation of wind stress estimated using the scatterometer data during 2007-2020 in the Indian Ocean. In the study region as a whole the wind stress decreased by 5.4% by including currents into the wind stress equation. The most significant reduction in the wind stress is found along the most energetic regions with strong currents such as Somali Current, Equatorial Jets and Aghulhas retroflection. A highest reduction of 11.5% is observed along the equator where the Equatorial Jets prevail. A sensitivity analysis has been carried out for the study region and for different seasons to assess the relative impact of winds and currents in the estimation of wind stress by changing the winds while keeping the currents constants and vice versa. The inclusion of currents decreased the wind stress and this decrease is prominent when the currents are stronger. This study showed that equatorial Indian Ocean is the most sensitive region where the current can impact on wind stress estimation. The results showed that uncertainties in the wind stress estimations are quite large at regional levels and hence better representation of wind stress incorporating ocean currents should be considered in the ocean/climatic models for accurate air-sea interaction studies.

The limitedness of the nonrenewable local energy resources in Israel, even in background of the later gas fields’ findings, continues to force the state to devote various efforts for the ‘green’ energy development. These efforts include installations both in the solar and in the wind energy, with a purpose to improve the diversity of energy sources. While the standard discounted cash flow (DCF) method using the net present value (NPV) criterion is extensively adopted to evaluate investments, the standard DCF method is inappropriate for the rapidly changing investment climate and for the managerial flexibility in investment decisions. In recent years, the real options analysis (ROA) technique is widely applied in many studies for valuation of renewable energy investment projects. Hence, we apply in this study the real options analysis approach for the valuation of wind energy turbines and apply it to the analysis of wind energy economic potential in Israel.

Integration of wind energy into the grid faces a great challenge regarding power quality. The International Electrotechnical Commission (IEC)~61400-21 standard defines the electrical characteristics that need to be assessed in a Wind Turbine (WT), as well as the procedure to measure the disturbances produced by the WT. One of the parameters to be assessed are voltage fluctuations or flicker. To estimate the flicker emission of a Wind Power Plant (WPP), the standard establishes that a quadratic exponent should be used in the summation of the flicker emission of each WT. This exponent was selected based on studies carried out in WPPs with type I and II WTs. Advances in wind turbines technology have reduced their flicker emission, mainly thaks to the implementation of power electronics for the partial or total management of the power injected into the grid. This work is based on measurements from a WPP with 16 type III WTs. The flicker emission of a single WT and of the WPP were calculated. Low flicker emission values at the Point of Common Coupling (PCC) of the WPP were obtained. The flicker estimation at the PCC, based on the measurement from a single WT, was analyzed using different exponents. The results show that a cubic summation performs better than the quadratic one in the estimation of the flicker emission of a WPP with type III WTs.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

This article presents a study conducted within a wind tunnel to enhance understanding of the excitation mechanism of stay cable vibration under arid conditions. Numerous wind tunnel tests were meticulously analyzed. Initially, the vibration of the stay cable was measured under steady flow conditions at a flow angle of 45 degrees and an inclination of 25 degrees, while varying wind velocities were applied. Additionally, an investigation into the flow field surrounding the stay cable was conducted in both vertical and horizontal directions. By utilizing two hot wire anemometers in the cable wake, an extensive database of flow field measurements was obtained. The experimental results revealed that the vibration characteristics of the stay cable under the arid conditions considered in this study aligned with findings reported in existing literature. Notably, a deeper comprehension of the excitation mechanism of a stay cable in a dry state was attained. This mechanism is closely associated with the inhibition of Karman vortices and the development of low-frequency vortices. At low wind speeds, Karman vortices predominated, resulting in small-amplitude vibrations. However, as the wind speed increased, the influence of Karman vortices diminished progressively, while the low-frequency vortices grew stronger. These low-frequency vortices exhibited high energy and a significant correlation with shedding along the stay cable, thereby inducing cable vibration in a dry environment.

The survey findings reveal that rivers worldwide carry an annual sediment load of 15 billion tons into the sea, and the Yellow River basin alone contributes 1.6 billion tons of sand. Therefore, understanding the science of wind and sand in the Yellow River is crucial to ensuring the safe development of similar basins across China and the world. This study examines the midstream wind and sand area of the Xiliugou tributary, a part of the upper Yellow River. The researchers used a stepped sand collector combined with an anemometer to measure the sand transport flux at 0-50 cm height on various underlying surfaces of the basin. Then, they estimated the amount of wind and sand entering the Yellow River using a function model based on the measured factors. Furthermore, the team analyzed the particle size composition of wind-eroded sand to better understand the principles of wind and sand erosion and accumulation in the basin. The results of the study show that the sand transport flux per unit area varies significantly across different underlying surfaces. Moreover, the contribution of moving sandy land, semi-fixed sand, and fixed sand to wind and sand deposition in the Yellow River basin was 77.08%, 15.30%, and 7.62%, respectively. The vertical change of sand transport rate on the basin's surface demonstrates that the total sand transport rate is an exponential function of wind speed. Based on this relationship, the researchers estimated that the total annual average wind and sand entering the Yellow River basin via the Xiliugou tributary is approximately 8.09×105t. Due to the basin's unique geography and sand source, the particle size composition of wind-eroded sand differs between the east and west sides of the river channel. On the west side, desert sand, mainly fine sand, and very fine sand constitutes the sand source. Conversely, on the east side, sand collected in the riverbed by secondary wind erosion is the primary sand source. Furthermore, human activities have disturbed the grain composition, mainly comprising powder and clay particles. This phenomenon, known as "wind-blown mud and water-washed sand," is evident in this geographical unit. In conclusion, the Yellow River basin still faces significant ecological security hazards. Understanding the coupling relationship between desert-basin-sand and wind is the foundation for effectively controlling wind and sand flow into the Yellow River basin.

With the rapid development of global offshore wind power, the demand for offshore wind power operation and maintenance is also increasing. Wisdomization of offshore wind farms is a practical need to improve the operation level and benefit of offshore wind farms. This paper first introduces the current development situation and characteristics of global offshore wind power, and expounds the current situation and main challenges of offshore wind power operation and maintenance market. Therefore, our paper discusses the innovation of offshore wind power operation and maintenance from the aspects of operation and maintenance management of offshore wind power, monitoring and analysis technology of units, far-reaching wind field monitoring and operation and maintenance risks. Then, combined with information technology and lean management concept, a smart operation and maintenance management platform for wind farms in far-reaching sea areas is built to explore centralized and intelligent operation and maintenance management mode, improve operation and maintenance efficiency of wind farms in far-reaching sea areas, and minimize operation and maintenance costs. Finally, through the research on the characteristics of 5G technology, combined with the practical experience of operation and maintenance, and in view of the characteristics of offshore wind farms, we analyze and propose several typical application scenarios of 5G technology in the intelligent operation and maintenance of offshore wind farms, which provides a new solution for the efficient operation and maintenance of offshore wind farms.

In this paper, dual-rotor counter-rotating (CR) configurations of vertical axis wind turbines (VAWT) are briefly inspected and divided into three types. This investigation was focused on one of these types – the CR-VAWT with co-axial rotors, in which two equal rotors are placed on the same shaft, displaced from each other along it and rotate in opposite directions. For this CR-VAWT with three-blade H-Darrieus rotors, the properties of the design in terms of aerodynamics, mechanical transmission and electric generator, as well as control system are analyzed. A new direct-driven dual-rotor (DR) permanent magnet synchronous generator (PMSG) was proposed, in which two built-in low-power PM electric machines have been added. They perform two functions – start-up and overclocking of the rotors to the angular velocity at which the lifting force of the blades is generated and stabilizing the CR-VAWT work as wind gusts act on the two rotors. Detailed in this paper is the evaluation of aerodynamic performance of the CR-VAWT via 3D computational fluid dynamics (CFD) simulations. The evaluation was conducted using the CONVERGE CFD software with the inclusion of the actuator line model for the rotor aerodynamics, which significantly reduces the computational effort. Obtained results show that both rotors, while they rotate in opposite directions, had a positive impact on each other: the power coefficients of the upper and lower rotors in the CR-VAWT increased by 5.5% and 13.3% respectively compared to the single-rotor VAWT at the optimal distance between the rotors of 0.3 from a rotor height.

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Sudden variation of aerodynamic loads is the potential source of safety accidents of high-speed train (HST). As a follow-up investigation on the aerodynamic response of a HST that enters a tunnel under crosswind environment, this paper focuses on the transient response of a HST’s safety indices based on the train–track coupling interaction model. Firstly, a wind–train–track coupling dynamic model is proposed by introducing transient aerodynamic loads into the vehicle–track system. Secondly, the temporal evolution of safety coefficients indicates that the train’s safety risk increases during tunnel entry with crosswind. Results show that the derailment coefficients and wheel load reduction rate during tunnel entry are not only larger than those in open air but also those inside the tunnel due to the sudden disappearance of wind excitation at the tunnel entrance. In addition, the characteristic wind curve, which is the wind velocity against the train speed, is presented for application based on the current specification of the safety criteria threshold. The investigation will be useful in assessing the safety risk of a running train subjected to other aerodynamic attacks, such as the coupling effect of infrastructure scenario and crosswind in windy area.

Presently, Jordan confronts a double substantial challenge of enhancing energy security and mitigating greenhouse gas emissions. One of the most encouraging avenues to attain the objectives of sustainable development, energy security, and environmental conservation is to enhance the integration of renewable energy into the generation of electricity. In pursuit of this goal, the Jordanian government intends to expand investments in the green energy sector, with solar and wind energy poised to play a key role in satisfying energy demands and cultivating an environmental sustainability. This paper aims to examine the distinctive dynamics, challenges, obstacles, and potential solutions corresponding to the establishment of community solar and wind farms in Jordan suburban areas. It aims to highlight the opportunities and barriers influencing the track of sustainable energy adoption in the country. Through a comprehensive analysis of the Jordanian context, the goal is to extract insights that can guide policies and practices. Additionally, the findings may serve as a valuable benchmark for other regions confronting similar challenges on their pursuit for a sustainable energy future.

Climate research shows that extreme weather events and their effects are increasing in frequency, severity, and impact. Extreme wind events can cause massive societal and economic damage. High wind occurrences and their causes must be identified because they are hard to define and understand. Extreme wind modeling affects wind power plant planning, installation, and building modeling. This applies to city, vegetation, and vegetational area modeling. The scientific literature utilizes statistical, dynamic (Weather Research and Forecasting-WRF), and machine learning methods to identify extreme wind speeds. This study explores the relationship between lower-level extreme wind speeds and upper-level parameters like Z500, and T850 based on cluster analysis. The aim is to explore the correlation between upper-level atmospheric dynamics and lower-level wind behavior. K-Means cluster analysis method was employed to classify Turkish regions with similar severe wind episodes. WRF model simulation findings from NEWA (New European Wind Atlas) project (2019) were used for that purpose. The NEWA project's wind speed data at 100m a.g.l. was used for cluster analysis. We simplify the process by grid point reduction and applying the top 2% percentile as threshold value. Self-Organized Maps (SOM) clustering was also applied using the upper-level variables.

The maritime sector, grapples with environmental challenges, but the adoption of "cold ironing" emerges as a promising solution, signifying a shift toward sustainability by providing energy to ships at port, reducing reliance on fossil fuels, and significantly lowering emissions, contributing to global climate change efforts. This transition towards renewable energy is especially crucial for islands like Lesvos, addressing challenges of high energy costs and environmental degradation resulting from reliance on imported fossil fuels, highlighting the potential for positive economic and environmental impacts. The existing literature highlights a research gap in the development of Renewable Energy Sources (RES) to support cold ironing operations, specifically due to the absence of specific data for the power demand needed in such operations and the lack of monitoring data for accurate calculations. This study's primary objective is to utilize real monitoring data from the port of Lesvos to evaluate the power demand for a potential cold ironing application. Additionally, the study aims to analyze the implementation of a wind power park to meet the energy needs of an electric port and evaluate the potential reduction in CO2 emissions. The database of Marine Traffic S.A. was utilized, while the determination of the energy used in-port per ship depended on factors such as the number of arrivals, the duration of hoteling, and the type of engine used by the ships. In addition, the software RETSCREEN was used for the energy analysis of a simulated 20 MW wind park that can support the in-port operations. The combined monthly energy demand for both main and auxiliary engines of shipping vessels at Mytilini port was assessed to be 6118 MWh and averaging a power demand equaled to 8.2 MW. The simulation analysis indicates that the annual electricity delivered to the port can be72080 MWh, with a significant portion remaining unused and exported to the grid, amounting to 14956 MWh annually. Due to demand fluctuations, in-port activities require an additional 924 MWh from the main electrical grid, which highlights the importance of additional measures like energy storage and demand-response practices for the gradual transition to 100% Renewable Energy Sources (RES) operations.

This paper is devoted to the analysis of fluctuations in the solar wind plasma and interplanetary magnetic field parameters observed by Solar Orbiter and WIND spacecraft at different scales, ranging from ~10^3 to 10^7 km. We investigated two long observation intervals when the spacecraft were near the Sun-Earth line, during which the distance between the spacecraft was 0.1 and 0.5 AU respectively. It is analyzed how the fluctuation properties in different types of SW, associated with quasi-stationary and transient solar phenomena, transform on the way from the Sun to the Earth. It is shown that the time series behavior of the bulk speed subject to the least change even at the large distance between the observation points, while the time series of the IMF magnitude, components and proton density were significantly transformed even at the relatively short distance. Regardless of the assumed change in the absolute value of the parameters, the conducted statistical analysis shows that the characteristics of their fluctuations (including the level of intermittency) remains near constant regardless of the distance between the observation points and depends mostly on the type of the solar wind.

Renewable energy research becomes increasingly necessary as it grows in importance. Wind turbines are an excellent method of harnessing wind energy, which is one of the most important renewable energy sources. In light of the importance of studying wind turbine performance, this study describes the background and principles of wind turbine operation. Computational fluid dynamics (CFD) was used to evaluate the hydrodynamic performance of two types of offshore and onshore wind turbines. Mechanical power and thrust force are calculated using the CFD model for both types of turbines in a steady state based on the number of blades and rotors. The results of three-dimensional simulations are compared with those of experimental testing. Based on the method and assumptions used, the offshore turbine's mechanical torque showed an average deviation of about 4%. Increased wind speed increased mechanical power and thrust, while turbulence caused irregular pressure distributions on surfaces as a result of turbulence intensity. Furthermore, as the free wind speed increases, the blade tip and hub speed will also increase, expanding the vortices created by that flow.

The close proximity of different buildings heights can cause disturbances in the working of the smoke and ventilation ducts of lower buildings, threatening the health and even the lives of residents. To define the influence of high-rise building on the work of the ducts of the neighbouring double-storied building, experimental investigations in the wind tunnel were conducted. On this basis, empirical equations and graphs were developed leading to determining the aerodynamic coefficients considering different wind directions and the height of ducts. The direction of the wind reveals a greater influence than the height of the ducts. Properly using deflectors or increasing the height of the duct ensures maintaining static rarefaction in the area of smoke and ventilation ducts. The creation of rarefaction ensures the reliability of the natural ventilation system and the safety of the health of residents.

The overcrowding of high-rise buildings in the city adversely affects the wind environment by changing the air currents in the surrounding areas. In particular, extreme climate phenomena caused by climate change are stronger and more frequent, causing social damages in cities. To comprehensively better understand the wind properties around high-rise buildings, actual filed measurement is necessary to determine the environment assessment of wind effect. We performed the on-site measurement on LCT residential complex regions (411.6m tall) with highly concentrated high-rise buildings in coastal city Busan, South Korea under extreme weather conditions such as typhoon invasions to determine wind fluid characteristics. In the field monitoring, five anemometers were installed to analyze the wind environment around high-rise buildings when typhoon 'Hinnamnor' invaded. Compared to the nearby weather station operated by KMA(Korea Meteorological Administration), the gust was 3.7 times stronger and the max 1 min-mean wind speed was 3.1 times stronger, and the characteristics of the downward wind and the canyon wind were shown depending on the location characteristics of the point. Turbulence intensity decreased as the wind speed increased and converged to a certain value. Likewise, the gust factor also decreased as the wind speed increased and converged to 2.0, which is considered to be the parameter that best represents the intensity of the instantaneous gust caused by the skyscraper wind effect.

Sea surface wind forecasts in the Adriatic Sea often suffer for unadequate modelling, especially for the wind speed. This has detrimental effects on the accuracy of sea level and storm surge predictions. We present a numerical method to reduce the bias between the sea surface wind observed by the scatterometers and that supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric model, for storm surge forecasting applications. The method, called “wind bias mitigation”, relies on scatterometer observations to determine a multiplicative factor ∆ws which modulates the standard model wind in order to decrease the bias between scatterometer and model. We compare four different mathematical approaches to this method, for a total of eight different formulations of the multiplicative factor ∆ws. Four datasets are used for the assessment of the eight different bias mitigation methods: a collection of 29 Storm Surge Events (SEVs) cases in the years 2004-2014, a collection of 48 SEVs in the years 2013-2016, a collection of 364 cases of random sea level conditions in the same period, and a collection of the seven SEVs in 2012-2016 that were worst predicted by the Centro Previsioni e Segnalazioni Maree, Comune di Venezia (Tide Forecast and Early Warning Centre of the Venice Municipality - CPSM). The statistical analysis shows that the bias mitigation procedures supplies a mean wind speed more accurate than the standard forecast, when compared with scatterometer observations, in more than 70% of the analyzed cases.

In this paper we present AWEbox, a Python toolbox for modeling and optimal control of multi-aircraft systems for airborne wind energy (AWE). AWEbox provides an implementation of optimization-friendly multi-aircraft AWE dynamics for a wide range of system architectures and modeling options. It automatically formulates typical AWE optimal control problems based on these models, and finds a numerical solution in a reliable and efficient fashion. To obtain a high level of reliability and efficiency, the toolbox implements different homotopy methods for initial guess refinement. The first type of methods produces a feasible initial guess from an analytic initial guess based on user-provided parameters. The second type implements a warmstart procedure for parametric sweeps. We investigate the software performance in two different case studies. In the first case study we solve a single-aircraft reference problem for a large number of different initial guesses. The homotopy methods reduce the expected computation time by a factor of 1.7 and and the peak computation time by a factor of 8, compared to when no homotopy is applied. Overall, the CPU timings are competitive to timings reported in the literature. When the user initialization draws on expert a priori knowledge, homotopies do not increase expected performance, but the peak CPU time is still reduced by a factor of 5.5. In the second case study, a power curve for a dual-aircraft lift-mode AWE system is computed using the two different homotopy types for initial guess refinement. On average, the second homotopy type, which is tailored for parametric sweeps, outperforms the first type in terms of CPU time by a factor of 3. In conclusion, AWEbox provides an open-source implementation of efficient and reliable optimal control methods that both control experts and non-expert AWE developers can benefit from.

This paper presents the super-twisting algorithm (STA) direct power control (DPC) scheme for the control of active and reactive powers of grid-connected DFIG. Simulations of 5 KW DFIG has been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC). The proposed controller schemes with fixed gains are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effe This paper presents a comparative study of two approaches for the direct power control (DPC) of doubly-fed induction generator (DFIG) based on wind energy conversion system (WECS). Vector Control (VC) and Sliding Mode Control (SMC). The simulation results of the DFIG of 5 KW in the presence of various uncertainties were carried out to evaluate the capability and robustness of the proposed control scheme. The (SMC) strategy is the most appropriate scheme with the best combination such as reducing high powers ripple, diminishing steady-state error in addition to the fact that the impact of machine parameter variations does not change the system performance. cts of parametric uncertainties not affecting system performance.

The incorporation of wind generation introduces challenges to the operation of the power system due to its uncertain characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal Kriging and analog approaches are used to forecast wind power generation and used as input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal Kriging takes into account the spatial and temporal information given by the database to enhance wind forecasts. We evaluate the performance of using the spatio-temporal Kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results show that the use of Kriging-based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

This paper reassesses the detrimental effect on fatigue performance due to thicker sections based on extensive fatigue strength test database, taken from research program worldwide over the past half of a century in offshore oil & gas and renewable industry. The data entries in the database have been evaluated to ensure its data integrity. Statistical analyses on these S-N data are performed with or without the thickness correction at different exposure level to corrosive environment, in order to re-evaluate the suitability of current standards in regard to the thickness effect. The study has concentrated on T-joint, transverse butt welded joint and tubular joint as these are the most commonly used joint types in the offshore wind industry. The analysis indicates general agreement of fatigue strength with the thickness effects in current standard for in air conditions but great conservatism for corrosive environment.

In this paper, we presented a method of retrieving sea surface wind speed from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide mode, which have been extensively acquired over the Arctic for sea ice monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve sea surface wind by spaceborne SAR, we introduced an alternative method based on physical model guided neural network. Parameters of SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of the backward propagation (BP) neural network, and the output is the sea surface wind speed. The network is developed based on more than 11,000 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic and their collocations with scatterometer measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind speed. Further comparison of the SAR retrieved sea surface wind speed with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of retrieval when the wind direction data of a reanalysis model are used as inputs to the neural network. By combining the detected sea ice cover information based on the EW data, one can expect to derive simultaneously sea ice and marine-meteorological parameters by spaceborne SAR in a high spatial resolution in the Arctic.

We present a comprehensive database of near-shore wind observations that were carried out during the experimental campaign of the RUNE project. RUNE aims at reducing the uncertainty of the near-shore wind resource estimates from model outputs by using lidar, ocean, and satellite observations. Here we concentrate in describing the lidar measurements. The campaign was conducted from November 2015 to February 2016 at the west coast of Denmark and comprises measurements from eight lidars, an ocean buoy and three types of satellites. The wind speed was estimated based on measurements from a scanning lidar performing PPIs, two scanning lidars performing dual synchronized scans, and five vertical profiling lidars, of which one was operating offshore on a floating platform. The availability of measurements is highest for the profiling lidars, followed by the lidar performing PPIs, those peforming the dual setup, and the lidar buoy. Analysis of the lidar measurements reveals good agreement between the estimated 10-m wind speeds, although the instruments used different scanning strategies and measured different volumes in the atmosphere. The campaign is characterized by strong westerlies with occasional storms.

With the rapid development of wind power generations, the inertial response of wind turbines (WTs) are widely concerned recently, which is important for grid frequency dynamic and stability. This paper recognizes and understands the inertial response of type-3 and type-4 WTs from the view of equivalent internal voltage, in analogy with typical synchronous generators (SGs). Due to the dynamic of the equivalent inertial voltage different from SGs, the electromechanical inertia of WTs is completely hidden. The rapid power control loop and synchronization control loop is the main reasons that the WT's inertial response is disenabled. On the basis of the equivalent internal voltage's dynamic, the existing inertia control method for WTs are reviewed and summarized as three approaches from the view of WT's control, i.e. optimizing the power control or synchronization control or both. At last, the main challenges and issues of these inertia controls are attempted to explain and address.

Current global commitments to reduce emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfill its obligations. However, no scoping study has yet been carried out. The methodology presented here addresses this research need. It employs Analytical Hierarchy Process and pairwise comparison methods in a Geographical Information Systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked, then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint, to deliver maximum turbine number. Ten favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/yr of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the paper, giving an initial plan for installation in these locations.

In this paper, the linear nonequilibrium thermodynamic approach is used to analyze energy processes in the wind energy conversion system (WECS), with a directly connected vertical axis wind turbine (VAWT) and vector controlled permanent magnet synchronous generator (PMSG). Both elements are considered as linear universal energy converters (EC) linearized at the points of the given range of common to VAWT and PMSG angular velocity. The dependences on the angular velocity of the coupling coefficients between the ECs input and output and the dimensionless parameters of their operating modes were received. This allows for choosing the optimal points of ECs operation according to the specified criteria and obtaining indicators of their maximum efficiency. To assess the quality of the cascade connection of two ECs, the appropriate coefficient was introduced, the dimensionless parameters and characteristics of the equivalent EC were obtained, and the conditions of its maximum efficiency were determined. Analysis of the VAWT-PMSG connection quality showed reserves for improving WECS efficiency. The impact of VAWT and PMSG variable parameters on the quality of this connection is studied, and further research directions are shown. The proposed thermodynamic approach allows an effective search for solutions for the efficiency improvement of various systems with energy transfer and transformation.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

This study introduces an integrated methodology that incorporates vessel motion dynamics into the evaluation of operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs). By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, the methodology accounts for vessel motion effects during offshore operations. The approach was demonstrated through a numerical case study at two wind farm sites, Marram Wind and Celtic Sea C, with a fictive wind farm layout of 100 × 15 MW NREL turbine on a UMaine VolturnUS-S platform. Three Major Component Replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy resulted in the highest O&M costs—94 k€/MW/year at Marram Wind and 97 k€/MW/year at Celtic Sea C—due to extended MCR durations (90-180 days), leading to lower availability (90%-94%). In contrast, the FTF and SHC strategies, which are still under development, demonstrate significantly lower costs and reduced downtime. The SHC strategy, in particular, has proven to be the most cost-effective, achieving up to a 64% reduction in costs while increasing availability to 97%-98%. The integrated approach incorporates vessel dynamics, accounting for factors such as wave direction, wave period, and vessel response to varying sea states. This allows for greater flexibility in setting operational limits, potentially permitting higher limits in favorable conditions where vessel motion impact is reduced. However, in scenarios where dynamic vessel responses lead to increased motions—such as when waves approach from the side or when the wave period is close to the vessel's natural roll period—more restrictive limits may be necessary, even if significant wave heights are lower. This flexibility or restriction highlights the importance of incorporating motion-based dynamics for emerging technologies in the evolving FOWT O&M market.

Due to its high short-term global warming potential (GWP) compared to carbon dioxide, methane (CH₄) is a considerable agent of climate change. This research is aimed at analyzing data on methane gathered at the GAW (Global Atmosphere Watch) station of Lamezia Terme (Calabria, Southern Italy) spanning seven years of continuous measurements (2016-2022) and integrating the results with key meteorological data. Compared to previous studies on detected methane mole fractions at the same station, daily-to-yearly patterns have become more prominent thanks to the analysis of a much larger data set. Overall, the yearly increase of methane at the Lamezia Terme station is in general agreement with global measurements by NOAA, though local peaks are present, and an increase linked to Covid-19 is identified. Seasonal changes and trends have proved to be fully cyclic, with the daily cycles being largely driven by local wind circulation patterns and synoptic features. Outbreak events have been statistically evaluated depending on their weekday of occurrence to test possible correlations with anthropic activities. A cross analysis between methane peaks and specific wind directions has also proved that local sources may be deemed responsible for the highest mole fractions.

Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real time pricing (RTP) schemes. In this paper, we present a new method to optimise the size and manage utility-scale wind-PV systems using PHS with energy arbitrage under RTP. The storage in the PHS is used to supply load consumption and/or energy arbitrage. Further, both load supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. The PHS is not worth considering with the present cost of components.

Achieving Net Zero emissions target is driving up the need for offshore wind as an alternate energy source. Ireland has the capacity to produce upwards of 30GWof power through offshore wind alone. Wind resource assessments are extremely vital in determining the long term trends of a site. The instruments used for this often suffer breakdowns or miss readings which impacts the long term trend analysis. Wind time series data from Ireland’s Marine Institute is used which is shown to have significant gaps in it’s 20 years of service. Data imputation is necessary to fill in these gaps as accurately as possible. This paper compares LSTMs and ARIMA as two competing methodologies for data imputation. LSTMs are shown to be marginally superior to ARIMA imputation with a mean squared error of 0.45 compared to that of ARIMA of 0.60.

Floating wind is becoming an essential part in terms of renewable energy. Therefore, highlighting the perspectives in developing the floating wind platforms is very important. In this paper, we focus on floating wind concepts and projects around the world. This will give a taste to the reader about what is going on in terms of the projects around the world. The main aim of this work is to further explain the collected data regarding the floating wind concepts and projects, and further classify them in terms of cost, power capacity, wind speed, water depth, and distance to shore.

This study investigates the spatial and temporal distribution of litter at 7 beach sectors in Cadiz Bay, Southwest Spain. Two ten-day surveys carried out in autumn 2022 and spring 2023 revealed a total of 4,199 and 4,634 items, respectively, with plastic being the predominant material (71.13% in autumn, 88.39% in spring). Litter quantities varied significantly among beaches, La Puntilla showing the highest abundance with 0.48 ± 0.25 items m-1 in autumn and 1.34 ± 0.61 items m-1 in spring. The lowest abundance was recorded at sector 1 of Valdelagrana beach (0.08 ± 0.06 items m-1) during autumn and at El Castillito beach (0.08 ± 0.04 items m-1) during spring. Statistical analyses did not reveal significant differences in litter quantities between autumn and spring, however high significant interaction was shown considering both season and beach lo-cation. Seasonal differences were also observed in litter composition. Litter categories increased from 90 in autumn to 107 in spring. The top 10 litter categories varied between seasons and included cigarette butts, plastic fragments and plastic packaging. Cigarette butts were principally abundant in autumn because re-lated to summer beach users and wet wipes were particularly observed in spring because linked to wastewaters and river discharges especially relevant in winter months. Hydrodynamic and wind conditions were analyzed, revealing complex relationships with litter abundance.

Super Typhoon Saola came very close to Hong Kong on 1 and 2 September 2023, necessitating the issuance of No. 10 hurricane signal, the highest tropical cyclone warning signal, in Hong Kong. While there were widespread damages in Hong Kong, no people were killed in the event with effective early warning. It is rare that a super typhoon came very close to Hong Kong and this paper is the first part in the series of the documentation of Saola to summarize the interesting observations of Saola near Hong Kong for future reference by weather forecasters, including sur-face observations, upper air observations, microburst alert from weather radars, and turbulence intensity based on spectral width measurement of radars.

Wind energy is a rapidly expanding renewable energy technique. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind-farm performance. In this research, we investigate the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind-turbine array on various rough topographies. First, we evaluated our LES method through mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyze the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results demonstrate that the ground roughness element’s flow resistance boosts large wind-farm power production by almost 68% in fully aerodynamic rough surface compared to flat terrain. Dispersive stress analysis revealed that the primary degree of spatial heterogeneity in the wind farm is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress is almost 45% to the overall drag. We also observed that the power performance of the wind farm in complex terrain outperforms the drag. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

The quantile estimation of extreme wind speed is needed in various environmental fields such as climatology, design of structures, renewable energy sources and agricultural operations. These calculations are crucial for the coding of wind speed. In this study, the required wind speed series of 16 stations in Khyber Pakhtunkhwa, Pakistan, was obtained from the NASA official website and measured in meters per second (m/s) at a 10-meter distance. A Regional Frequency Analysis of 16 AMWS stations was performed using L-moments. The quantile estimates of extreme wind speed are needed for various areas of interest using Regional Frequency Analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meter per second (m/s). A Regional Frequency Analysis of AMWS using L-moments is performed utilizing wind speed data from sixteen sites (16) in Pakistan's Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions are chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

To deal with the uncertainties in modeling offshore wind turbines, we proposed a parameter inversion method for the pile-soil interaction model based on structural health monitoring results and the numerical model. The proposed parameter inversion method has a numerical model, an objective function selected using both the numerical and identified results, and an inverse optimization using a random search algorithm in the assumed parameter space. The parameter results in the minimum optimization objective function are identified as the in-situ parameter. The proposed method is confirmed to converge after some iterations, whatever the initial parameter values are. However, different initial parameter cases may converge to slightly different optimal parameters, implying the pile results are sensitive to geological parameters. Moreover, a comparison with the original design results shows design redundancy or risks. Though the proposed method has several flaws, it can shed some light on the influence of uncertainties in offshore wind turbines, such as soil parameters in geological surveys.

The aim of this paper is to provide a new perspective on the conditions for the multiple dimensions of concerns, and to understand why some wind power projects run smoothly while others lead to unexpected outcomes due to local resistance. The paper described how multiple community concerns about wind power project planning can be described using conflict management theory and by using different procedures to handle tangible and intangible concerns. Policy recommendations are given on how: to minimise overflowing and the risk of the conflicts; to avoid “boxing” community concerns in categories; to give more attention to the intangible and less calculable concerns; to secure appropriate and early planning with proactive involvement of local communities.

In recent years, excessive heat in the urban texture has become the main problem in the humid and calm wind city of Babolsar with high density, especially in high-rise areas. Therefore, in order to create comfort in this region, it is necessary to establish and continue the wind circulation in space with an environmentally compatible and optimal configuration. The study applies combination of literature, field measurement, experimental validation of CFD simulation output, and comparative analysis. After field measurement and validation of FLOW-3D simulation software (V11.2.2), the relationship between these parameters (height, the width of passages, enclosure between buildings, and buildings' orientation) will be studied which affects the wind’s velocity and direction. The factors of the buildings' orientation and enclosure based on the passages' width have opposite reactions in the direction of the prevailing wind especially from perpendicular side. In this study, two effective factors are on wind velocity: 1- The orientation of the buildings towards the wind flow by creating permeability 2- Reducing the enclosure by increasing the width of the passage's perpendicular to the wind flow (w '= 3w, E' = 0.33E). According to the theoretical and practical study, first, the creation of permeability in the body of the block and the separation of buildings instead of aggregation has been studied, and then reducing the confinement of streets perpendicular to the wind flow has been discussed as effective solutions to improve the wind velocity and circulation between the urban environment.

Oil palm is one of the most important crops in Malaysia. Lately, the production of oil palm has been reduced due to a variety of factors, including the weather and climate. Temperature, wind speed, relative humidity, sunshine, and rainfall distribution all have an impact on palm tree growth and development, which in turn has an impact on oil palm production. This paper aims to investigate the effects of some weather elements (temperature, wind speed, relative humidity, sunshine, and rainfall) on oil palm production in Peninsular Malaysia. Data were analyzed using the Statistical Package for Social Sciences (SPSS 20.0 version), with descriptive statistics, and multiple linear regression (MLR). The MLR model determined the strength of the relationship between oil palm yield (dependent variable) and the changing variables of temperature, sunshine, wind speed, relative humidity, and rainfall (independent variables). The findings revealed that temperature, wind speed, relative humidity, sunshine, and rainfall have a low impact on oil palm production and yield turnover. The R2 value of 0.202 shows that the independent variables explained only 20.2% of the fluctuation in palm oil production. The study recommends working within an integrated approach involving scientific research, planting, improving variety, improving regional academic leadership, and engaging private and public stakeholders, emphasized collaborative efforts with researchers in consumer countries, and strengthening the capacity of growers to best agroecological practices.

Wind is one of the factors that has a great influence on suspended matter in lakes, especially in shallow lagoons. In order to know how wind affects the water in Albufera of Valencia, a shallow coastal lagoon, the measured variables of turbidity and transparency have been correlated with the estimates by processing Sentinel-2 satellite images with the Sen2Cor processor. Data from four years of study show that most of them are light to gentle easterly breezes and moderate to fresh westerly breezes. The results obtained show significant correlations between the measured variables and those obtained from the satellite images for total suspended matter and water transparency and with the average daily wind speed. There is no significant correlation between wind and chlorophyll a. Moderate to fresh breezes resuspend the fine sediment reaching concentration values from 100 to 300 mg L-1 according to satellite data. However, it is necessary to obtain field data for the values of moderate and fresh winds, as for now there are no experimental data to verify the validity of the satellite estimates.

Amplitude modulation (AM) is a characteristic feature of wind farm noise and has the potential to contribute to annoyance and sleep disturbance. This study aimed to develop an AM detection method using a random forest approach. The method was developed and validated on 6,000 10-second samples of wind farm noise manually classified by a scorer via a listening experiment. Comparison between the random forest method and other widely-used methods showed that the proposed method consistently demonstrated superior performance. This study also found that a combination of low-frequency content features and other unique characteristics of wind farm noise play an important role in enhancing AM detection performance. Taken together, these findings support that using machine learning-based detection of AM is well suited and effective for in-depth exploration of large wind farm noise data sets for potential legislative and research purposes.

In-cloud ice mass accretion on wind turbines is a common challenge faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests--including the cloud water response to surface roughness and albedo effects related to them--must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and to present date not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME 3D NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insulation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In early season cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.

The application of naturally ventilated pig buildings (NVPBs) with outdoor exercise yards is on the rise mainly due to animal welfare considerations, while the issue of emissions from the buildings to the surrounding environment is important. Since air pollutants are mainly transported by airflow, the knowledge on the airflow characteristics downwind the building is required. The objective of this research was to investigate airflow properties downwind of a NVPB with a roofed outdoor exercise yard for roof slopes of 5°, 15°, and 25°. Air velocities downwind a 1:50 scaled NVPB model were measured using a Laser Doppler Anemometer in a large boundary layer wind tunnel. A region with reduced mean air velocities was found along the downwind side of the building with a distance up to 0.5 m (i.e. 3.8 times building height), in which the emission concentration might be high. It was found that a smaller roof slope (i.e. 5° slope) resulted in a higher and shorter wake zone and thus a shorter air pollutant dispersion distance. It was concluded that a smaller roof slope could contribute to the dilution of air pollutants and a lower air pollutant concentration near the ground.

In 2015, the new installed capacity of global renewable energy power generation exceeded the newly installed capacity of conventional energy power generation, marking a structural change in the construction of the global power system. With the continuous improvement of wind energy utilization technology, the global wind power industry has developed rapidly in recent years. The world's available wind energy is 20 billion kilowatts and has become one of the most economical green power. In China, wind power has become the third largest source of electricity, with the installed capacity increasing from 3.1% in 2010 to 9.2% in 2017. In 2017, China's new installed capacity was 19,660 MW, accounting for 37.45% of the world's new installed capacity. This paper evaluates and compares the efficiency of wind power industry in the four regions of eastern, central, western and northeastern China through EBM models based on radial and non-radial factors. This paper discusses the contribution of China's wind power industry to CO2 emission reduction from the relationship between installed capacity efficiency and CO2 emission reduction efficiency. The conclusions show that the overall efficiency score and ranking of wind power in 2013-2017 is the best in the eastern region, followed by the northeast region and the western and central regions.

An open source computational fluid dynamics (CFD) solver has been incorporated into the WindNinja modeling framework widely used by wildland fire managers as well as researchers and practitioners in other fields, such as wind energy, wind erosion, and search and rescue. Here we describe incorporation of the CFD solver and evaluate its performance compared to the conservation of mass (COM) solver in WindNinja and previously published large-eddy simulations (LES) for three field campaigns conducted over isolated terrain obstacles of varying terrain complexity: Askervein Hill, Bolund Hill, and Big Southern Butte. We also compare the effects of two important model settings in the CFD solver and provide guidance on model sensitivity to these settings. Additionally, we investigate the computational mesh and difficulties regarding terrain representation. Two important findings from this work are: (1) the choice of discretization scheme for advection has a significantly larger effect on the simulated winds than the choice of turbulence model and (2) CFD solver predictions are significantly better than the COM solver predictions at windward and lee side observation locations, but no difference was found in predicted speed-up at ridgetop locations between the two solvers.

Wind tunnels are devices that enable researchers to study the flow over objects of interest, the forces acting on them and their interaction with the flow, which is nowadays playing an increasingly important role due to noise pollution. Since the first closed circuit wind tunnel with variable cross-section was built in G¨ottingen, its Prandtl configuration has little change. The wind tunnel with Prandtl configuration has four corners and vanes, more than 50% of the total pressure loss are caused by the corners and vanes. How to reduce the total pressure loss is a world class problem in the wind tunnel design. This study attempts to propose a novel configuration of wind tunnel, where the corners have been replaced by semi-circular tunnel. Sun wind tunnel 2 has only two corners and vanes, while Sun wind tunnel 1 has no corners and vanes at all. It is expected the new wind tunnel can reduce the total pressure loss from 50% to 10%.

Failure of wind turbines is a multi-faceted problem and its monetary impact is often unpredicted. In this study, we present a novel application of survival analysis on wind turbine reliability performance that includes accounting of previous failures and history of scheduled maintenance. We investigate the operational, climatic and geographical factors which affect wind turbine failures and model the risk rate of wind turbine failures based on data from 109 turbines in Germany operating during a period of 19 years. Our analysis showed that adequately scheduled maintenance can increase the survivorship of wind turbine systems and electric subsystems up to 2.8 and 3.8 times, respectively compared to the ones without scheduled maintenance. Geared-drive wind turbines and their electrical systems were observed to have 1.2- and 1.4-times higher survivorship, respectively, compared to direct-drive turbines and their electrical systems. It is also found that survivorship of frequently-failed wind turbine components, such as switches, is worse in geared-drive than in direct-drive wind turbines. We show that survival analysis is a useful tool for guiding the reduction of operating and maintenance costs of wind turbines.

Solar and wind resources available for power generation are subject to variability due to meteorological factors. Here, we use a new global climate reanalysis product, Version 2 of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), to quantify interannual variability of monthly-mean solar and wind resource from 1980 to 2016 at a resolution of about 0.5 degrees. We find an average coefficient of variation (CV) of 11% for monthly-mean solar radiation and 8% for windspeed. Mean CVs were about 25% greater over ocean than over land, and, for land areas, were greatest at high latitude. The correlation between solar and wind anomalies was near zero in the global mean, but markedly positive or negative in some regions. Both wind and solar variability were correlated with values of climate modes such as the Southern Oscillation Index and Arctic Oscillation, with correlations in the Northern Hemisphere generally stronger during winter. We conclude that reanalysis solar and wind fields could be helpful in assessing variability in power generation due to interannual fluctuations in the wind and solar resource. Skillful prediction of these fluctuations seems to be possible, particularly for certain regions and seasons, given persistence or predictability of climate modes with which these fluctuations are associated.

In this paper, the technical aspects of a multi-lidar instrument, the long-range WindScanner system, will be presented accompanied by an overview of the results from several field campaigns. The long-range WindScanner system consists of three spatially separated coherent Doppler scanning lidars and a remote master computer that coordinates them. The lidars were carefully engineered to perform arbitrary and time controlled scanning trajectories. Their wireless coordination via the master computer allows achieving and maintaining lidars’ synchronization within ten milliseconds. As a whole, the long-range WindScanner system can measure an entire wind field by emitting and directing three laser beams to intersect, and then by moving the beam intersection over the points of interest. The long-range WindScanner system was developed to tackle the need for high-quality observations of wind fields from scales of modern wind turbine and wind farms. It has been in operation since 2013.

Nacelle-based Doppler wind lidars have shown promising capabilities to assess power performance, detect yaw misalignment or perform feed-forward control. The power curve application requires uncertainty assessment. Traceable measurements and uncertainties of nacelle-based wind lidars can be obtained through a methodology applicable to any type of existing and upcoming nacelle lidar technology. The generic methodology consists in calibrating all the inputs of the wind field reconstruction algorithms of a lidar. These inputs are the line-of-sight velocity and the beam position, provided by the geometry of the scanning trajectory and the lidar inclination. The line-of-sight velocity is calibrated in atmospheric conditions by comparing it to a reference quantity based on classic instrumentation such as cup anemometers and wind vanes. The generic methodology was tested on two commercially developed lidars, one continuous wave and one pulsed systems, and provides consistent calibration results: linear regressions show a difference of ∼ 0.5 % between the lidar-measured and reference line-of-sight velocities. A comprehensive uncertainty procedure propagates the reference uncertainty to the lidar measurements. At a coverage factor of two, the estimated line-of-sight velocity uncertainty ranges from 3.2 % at 3 m s⁻¹ to 1.9 % at 16 m s⁻¹. Most of the line-of-sight velocity uncertainty originates from the reference: the cup anemometer uncertainty accounts for ∼ 90 % of the total uncertainty. The propagation of uncertainties to lidar-reconstructed wind characteristics can use analytical methods in simple cases, which we demonstrate through the example of a two-beam system. The newly developed calibration methodology allows robust evaluation of a nacelle lidar’s performance and uncertainties to be established in order to further be used for various wind turbines’ applications in confidence.

To reduce the cost of small wind turbines, a prototype of a butterfly wind turbine (6.92 m in diameter), a small vertical-axis type, was developed with many parts made of extruded aluminum suitable for mass production. An over-speed control system with movable arms that operated using centrifugal and aerodynamic forces was installed for further cost reduction. Introducing this mechanism eliminates the need for large active brakes and expands the operating wind speed range of the wind turbine. However, although the mechanism involving the use of only bearings is simple, the violent movement of the movable arms can be a challenge. To address this in the present study, dampers were introduced on the movable arm rotation axes to improve the movement of the movable arms. To predict the behavior of a movable arm and the performance of the wind turbine with the mechanism, a simulation method was developed based on the blade element momentum theory and the equation of motion of the movable arm system. Comparison of experiments and predictions with and without dampers demonstrated qualitative agreement. In the case with dampers, measurements confirmed the predicted increase in the rotor rotational speed when the shorter ailerons installed perpendicularly to the movable arms were used to achieve the inclination. The movable arm system with dampers is expected to be a useful device for vertical-axis wind turbines that are difficult to control.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

The increasing deployment of large-scale wind turbines in place of conventional generators is expected to lead to the dominance of asynchronous power sources in future power systems, further accelerating the trend toward grid electrification. As a result, the ability of power sources to support system voltage and frequency is gradually diminishing. Synchronous condensers (SC), which are synchronous machines operating without prime movers, serve as effective devices for providing both dynamic voltage support and inertia. They can significantly enhance the system’s capacity to maintain voltage and frequency stability. However, most existing studies on the optimization of synchronous condenser configurations tend to focus on only one aspect at a time, rather than addressing both simultaneously, limiting the full potential of these devices. Optimizing either voltage or frequency in isolation often results in suboptimal improvements in the other. Moreover, simultaneous optimization of both voltage and frequency can lead to non-convergent outcomes, complicating the search for an optimal solution. To address this, the paper proposes a hierarchical optimization strategy for synchronous condenser configuration aimed at enhancing both voltage and frequency stability. First, the configuration sites for the synchronous condensers are determined based on short-circuit ratio (SCR) constraints. Next, an outer layer optimization model is developed to minimize the total installed capacity of the condensers, while taking into account SCR and transient overvoltage levels as constraints. Following this, an inner layer optimization model is introduced, incorporating and as constraints. The model is solved using the bacterial foraging optimization algorithm (BFOA). Finally, a case study of a power grid with a high proportion of wind power validates the effectiveness of the proposed synchronous condenser configuration strategy. Compared to traditional methods, the total required capacity of synchronous condensers was significantly reduced.

The sound power LW of wind turbines (WTs) over the past decades has developed in relation to their electric power P as LW ∝ logP. The spectral distribution of the sound did not change significantly over time. This applies to the average performance of wind turbines, with differences between individual WT types. Because of the expected growth of onshore wind energy, a greater number of people will be living close to wind farms. This sustains the need for sound reduction. Sound reduction measures, such as serrations, reduced tip speed and low noise modes, may counteract the development of higher sound power from ever bigger WTs. To investigate this, the sound production of WT types over the last decades is analysed in relation to their size and electric power and the application of sound reduction measures. The analysis includes the broad band A-weighted and low frequency sound power levels as well as more detailed spectral distributions. Results show that the sound power level of wind turbines above 3 MW on average increases less with size than smaller turbines did. This is due to a lower blade tip speed and the use of sound reducing serrations. The spectral content of wind turbine sound, including the lower frequencies, has not changed significantly.

In this study, we investigate a yaw control strategy in a two-turbine wind farm with 3.5 MW turbines, aiming to optimize power management. The wind farm is equipped with a nacelle-mounted multi-plane LiDAR system for wind speed measurements. Using an analytical model and integrating LiDAR and SCADA data, we estimate wake effects and power output. Our results show a 2% power gain achieved through optimal yaw control over a year-long assessment. The wind predominantly blows from the southwest, perpendicular to the turbine alignment. The optimal yaw and power gain depend on wind conditions, with higher turbulence intensity and wind speed leading to reduced gains. The power gain follows a bell curve across the range of wind inflow angles, peaking at 1.7% with a corresponding optimal yaw of 17 degrees at an inflow angle of 12 degrees. Further experiments are recommended to refine the estimates and enhance the performance of wind farms through optimized yaw control strategies, ultimately contributing to the advancement of sustainable energy generation.

The development and utilization of wind energy is of great significance to the sustainable development of China's economy and the realization of the goal of "dual carbon". Under the typhoon system, the randomness and volatility of wind speed become an important basis for affecting the energy efficiency utilization and design of wind turbines. Therefore, based on wind speed and wind direction data at 10m, 30m, 50m and 70m heights of a wind power tower in Yancheng, Jiangsu Province, this paper analyzes the change characteristics of wind speed and wind direction by using BFAST method and Hurst index. Based on typhoon body data, topographic data and mesoscale system wind direction data, the causes of the occurrence and development of wind speed and wind direction of wind tower are further analyzed, and a conclusion is drawn: (1) In BEAST method, there are 5, 5, 6 and 6 change points at the height of 10m, 30m, 50m and 70m respectively. Among them, the change points at the height of 10m, 30m and 50m all change before and after node 325, and the change time point at the height of 70m is inconsistent with other heights. Hurst index results show that the inconsistency at 70m altitude is stronger than that at other altitudes. (2) The wind direction sequence at 10m, 30m, 50m and 70m altitude is fitted by stages, and the direction of wind direction is SE-E-SE-SW-W-NW, where, At the height of 70m, the deviation of the fitting trend line from other altitudes is larger in the wind speed strengthening stage and weakening stage. (3) The wind speed at the height of 10m and 70m has different response degrees to the typhoon body. The correlation between the wind speed and the distance between the wind measurement tower and the typhoon near the center is stronger at the height of 10m than at the height of 70m. The surface type and the wind direction of the mesoscale system also have certain effects on the wind speed and direction. In this paper, the change characteristics and influencing factors of wind speed and direction of wind tower are studied, which provides methods and theoretical support for the study of short-term wind speed when typhoon passes through, and provides reliable support for the selection of wind power forecast indicators and the selection and design of wind turbines under extreme gale weather systems.

This paper delves into the application of digital twin monitoring techniques for enhancing offshore floating wind turbine performance, with a detailed case study. We explore the practical implementation of digital twins and their efficacy in optimizing operations and predictive maintenance. Our findings emphasize digital twins' potential as a valuable tool for renewable energy, driving efficiency and sustainability in offshore floating wind installations.

: AliCPT-1 is the first Cosmic Microwave Background (CMB) experiment in China, dedicated to achieve accurate measurements of B-mode polarization. Situated in Ali of Tibet, China, this telescope is currently undergoing deployment and will operate in two frequency bands centered at 90 and 150 GHz. The Far-Field Flat Mirror（FFF）is a calibration device of AliCPT-1 telescope for far-field beam mapping. The design of FFF is optimized for easy assembly and adjustment. Meteorological station data reveals that the maximum wind speed near FFF is 17.5 m/s, while the maximum wind speed on the windward side is 8 m/s. The wind pressure on FFF was analysed using a maximum wind speed of 17.5m/s as the input condition, based on the fluid-structure coupling method in ANSYS. The results demonstrate that it is safe and reliable when withstanding combined gravity and wind pressure loads. The torque on the mount is within the motor rated torque. The flatness of the FFF reflective surface can be adjusted to an RMS value of 0.05 mm when taken into account the effect of gravity and assembly accuracy. The deformation caused by the maximum wind loads is approximately 0.0587 mm under the protection of wind-proof wall. The combined deformation is 0.077 mm in RMS value combining the two influences, which is less than 1/20 of wavelength. The FFF mirror assembly is stable and precisely for telescope calibration.

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net 1 Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, extreme weather periods are occurring in a cyclical manner, with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.

Offshore Wind Turbines (OWT) with increasingly higher energy output are being developed to meet energy demand, posing challenges for their foundation design. Several foundation types are used to support these turbines with monopiles (MP) accounting for 80% of the installed capacity. In this study, three-dimensional (3D) nonlinear finite element models (FEM) were employed to investigate the behaviour of monopile foundation supporting a 5MW wind turbine when subjected to lateral loading. Results indicate that the MP behavior depending on the pile length to diameter (L/D) ratio and the soil shear strength. Inspection of bending moment profiles at the lateral ultimate capacity indicated the monopiles can behave in flexible manner even with low L/D ratio. The L/D ratio affected the MP normalized lateral ultimate capacity at varying degrees and the biggest effect was for soft clays, amounting to around 5 folds increase for L/D values of 3.33 to 13.33. Lesser effects were found for stiff clays.

In this paper, solar irradiance and wind speed forecasts were performed considering time horizons ranging from 10 min to 60 min, under a 10 min time-step. Global horizontal irradiance (GHI) and wind speed were computed using four forecasting models (Random Forest, k-Nearest Neighbours, Support Vector Regression, and Elastic Net) to compare their performance against two alternative dynamic ensemble methods (windowing and arbitrating). Forecasting models and dynamic forecasting ensembles were implemented in Python for performance evaluation. The performance comparison between the prediction models and the dynamic ensemble methods was carried out by evaluating the RMSE, MAE, R² and MAPE, to evaluate whether the dynamic ensemble forecasting method obtained greater. According to the results obtained windowing dynamic ensemble method was the most efficient among the tested. For the wind speed data, by varying its parameter λ (from 1 to 100), a variable performance profile was obtained, where from λ =1 to λ = 74, windowing proved to be the most efficient, reaching maximum efficiency for λ = 19. Windowing was the best method for the GHI analysis, reaching its best performance for λ = 1. The efficiency gain using windowing was 0.56% when using the wind speed model and 1.96% for GHI.

Due to the special terrain of Urumqi (in the valley area), which often triggers strong foehn weather, and causes huge losses to local people's lives and social economy. This study uses in-situ surface meteorological variables (including the hourly temperature, pressure, humidity, wind, etc.) from the Urumqi Meteorological Station (downstream station, DS) and the Dabancheng Meteorological Station (upstream station, US) in the Middle Tianshan Valley and NCEP/NCAR reanalysis data from 2008 to 2022. A dataset of the foehn weather process at DS in the past 15 years is established and the variation patterns of each meteorological variable during the process of foehn is analyzed. Based on the physical mechanism of the occurrence of foehn, a three-element identification standard for foehn in Urumqi is established by comparing and analyzing the variation of wind direction (WD), wind speed (WS), and the difference of potential temperature (Δθ) between DS and US during the period of foehn and non-foehn from 2013 to 2022, namely: 94°≤ 2-minute average WD ≤168°, 2-minute average WS ≥2.0 m/s, and Δθ between DS and US ≥ 0.29 K. According to the test and evaluation, the three-element identification standard has an accuracy of 82.96%, and a hit rate of 89.50% for the occurrence of foehn in Urumqi from 2008 to 2012. Moreover, the hit rate of foehn identification is 100% for strong wind or above (i.e., 2-minute average WS ≥10.8 m/s) WS. In addition, combined with two predictors of sea-level pressure difference (ΔP) and Δθ between DS and US, forecasting foehn can be more accurately predicted than a single forecasting factor. When ΔP ≤ -12 hPa and Δθ≥5 K, the probability of the occurrence of foehn is more than 90%. This study provides certain reference and application value for the weather forecasting operation of foehn.

The fractal dimension of geomagnetic variations at the Baigazan magnetic station at Russian Altay for 2011-2013 was calculated using the Higuchi method. The daily variation of Higuchi Fractal Dimension (HFD) for the D,H,Z-components of the geomagnetic field has been investigated, its contribution to the variability of HFD is from 30 to 40 percent of the total variance. A correlation analysis of the fractal dimension of the variations of the D,H,Z-components with Auroral Electrojet (AE) index and solar wind characteristics was carried out. A negative correlations with logarithms of AE-index, interplanetary magnetic field (IMF) strength and solar wind velocity were found. About 25 percent of the fractal dimension variance is controlled by the variability of these characteristics. Pair and partial correlation coefficients for these parameters were calculated for every month of 2011-2013.

This study explores the feasibility of using an oscillating plate downstream of a cylindrical body to produce mechanical energy from a Von Kármán vortex street. The study aims to quantify the impact of the plate length, its separation from the cylinder, and a machine damping factor on the power coefficient and the blade’s displacement to identify the optimal configuration. This preliminary assessment assumes that the plate oscillation is small enough to avoid changes in the vortex dynamics. This assumption allows the construction of a surrogate model using CFD to evaluate the effect of plate length and separation from the cylinder on the fluctuating lift forces over the plate. Later, the surrogate model, combined with varying machine damping factors, facilitates the description of the device’s dynamics through the numerical integration of an angular momentum equation. The results showed that a plate with 0.52D length, 5.548D separation from the cylinder, and a damping factor of 0.013 achieved a power coefficient of 0.147 and a perpendicular displacement of 0.266D. These results demonstrate a substantial improvement in the performance of bladeless generators.

Newly build offshore wind farms (OWFs) render a collision risk between ships and installations. The paper proposed a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method is tested in offshore water in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.

The prediction of an extremely severe cyclonic storm (ESCS) is one of the challenging issues due to increasing intensity and its life period. In this study, an ESCS Fani that developed over Bay of Bengal region during 27 April - 4May, 2019 and made landfall over Odisha coast of India is investigated to forecast the storm track, intensity and structure. Two numerical experiments (changing two air-sea flux parameterization schemes; namely FLUX-1 and FLUX-2) are conducted with the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) model by using a moving nest with fine horizontal resolution about 3 km. The high resolution (25 km) NCEP operational Global Forecast System (GFS) analysis and forecast datasets are used to derive the initial and boundary conditions, the ARW model initialized at 00 UTC 29 April 2019 and forecasted for 108 hours. The forecasted track and intensity of Fani is validated with available India Meteorological Department (IMD) best-fit track datasets. Result shows that the track, landfall (position and time) and intensity in terms of minimum sea level pressure (MSLP) and maximum surface wind (MSW) of the storm is well predicted in the moving nested domain of the WRF model using FLUX-1 experiment. The track forecast errors on day-1 to day-4 are ~ 47 km, 123 km, 96 km, and 27 km in FLUX-1 and ~54 km, 142 km, 152 km and 166 km in Flux-2 respectively. The intensity is better predicted in FLUX-1 during the first 60 h followed by FLUX -2 for the remaining period. The structure in terms of relative humidity, water vapor, maximum reflectivity and temperature anomaly of the storm is also discussed and compared with available satellite and Doppler Weather Radar observations.

Wind energy in Europe is aimed to grow at a steady, high pace, but opposition from residents to local wind farm plans is one of the obstacles to further growth. A large body of evidence shows that local populations want to be involved and respected for their concerns, but in practice this is a complex process that cannot be solved with simple measures such as financial compensation. The visual presence and the acoustic impact of a wind farm is an important concern for residents. Generally environmental noise management aims to reduce the exposure of the population, usually based on acoustics and restricted to a limited number of sources (such as transportation or industry) and sound descriptors (such as Lden). Individual perceptions are taken into account only at an aggregate, statistical level (such as percentage of exposed, annoyed or sleep-disturbed persons in the population). Individual perceptions and reactions to sound vary in intensity and over different dimensions (such as pleasure/fear or distraction). Sound level is in fact a weak predictor of the perceived health effects of sound. The positive or negative perception of and attitude to the source of the sound is a better predictor of its effects. This article aims to show how the two perspectives (based on acoustics and on perception) can lead to a combined approach in the management of a wind farm aimed to reduce annoyance, not primarily of sound level. An important aspect in this approach is what the sound means to people: is it associated with the experience of having no say in plans, does it lead to anxiety or worry, is it appropriate? The available knowledge will be applied to wind farm management: planning as well as operation.

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method called the WeDoWind Ecosystem is developed and demonstrated. The method is centred around specific "challenges", which are defined by "challenge providers" within a topical "space" and made available to participants via a digital platform. The data required in order to solve a particular "challenge" is provided by the "challenge providers" under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the methods perform significantly better than EDP’s existing method in terms of Total Prediction Costs (saving up to €120,000). The WeDoWind Ecosystem is found to be a promising solution for enabling co-innovation in wind energy, providing a number of tangible benefits for both challenge and solution providers.

The public interest towards renewable and clean energy is an important part of shaping consumer behavior and policy towards these topics. A “big data” approach towards assessing the public interest for various topics consists in using freely available search frequency data for the Google search engine, through the Google Trends service. The search data frequency can be used to assess public opinion for a variety of topics, such as medicine, climate change and environmental concerns, finance, and economics etc. A study of the public interest towards wind energy topics is reported here. Six Google search keywords (“Wind power”, “Wind energy”, “Offshore wind”, “Wind farm”, “Wind turbine” and “Wind generator”) were investigated in the 2004–2020-time range. All keywords except “Offshore wind” show a steady decrease from a 2008 – 2010 maximum up to 2015, followed by a period limited change in the 2015 – 2020 range. The interest towards offshore wind topics follows a similar trend but increases in frequency starting from 2015 and reaches a maximum in 2018. Overall, the Google Trends data show a decrease of public interests towards most wind energy topics, with the exception of “Offshore wind”, for English speaking users, in the 2004–2020-time range.

Electric field assisted combustion is an important means to improve fuel combustion efficiency. This paper conducts extensive research on flame characteristics under different forms and different application methods of electric fields, emission of soot particles and simulation status. Different flame parameter measurement methods will lead to different degrees of error, and perfect numerical simulation can make simple predictions on experimental data. Most of the current numerical simulations are in two dimensions, and it is necessary to develop a complete and accurate three-dimensional model to simulate and predict the characteristics of the flame under an electric field. The emission of soot particles is also affected by the electric field, and reasonable electric field parameters can greatly reduce the emission of soot particles. It is recommended to conduct centralized measurement of different fuels under the electric field under high pressure and temperature conditions, so as to be able to develop a wider and more accurate flame dynamics and chemical model under the electric field.

A Life Cycle Analysis was performed considering three existing power plants of comparable size operating with different sources of renewable energy: geothermal, solar and wind. Primary data were used for building the life cycle inventories. The geothermal power plant includes emissions treatment for removal of hydrogen sulfide and mercury. The scenario about the substitution of natural emissions from geothermal energy, with specific reference to the greenhouse effect, is also investigated performing a sensitivity analysis. The results are characterized employing a wide portfolio of environmental indicators employing the Recipe 2016 and the ILCD 2011 Midpoint+ methods; normalization and weighting are also applied using the Recipe 2016 method at endpoint level. The results demonstrate a good eco-profile of geothermal power plant with respect to other renewable energy systems and allow for a critical analysis to support potential improvements of the environmental performances.

Large scale coherent structures in atmospheric boundary layer (ABL) are known to contribute to the power generation in wind farms. In the current paper, we perform a detailed analysis of the large scale structures in a finite sized wind turbine canopy using modal analysis from three dimensional proper orthogonal decomposition (POD). While POD analysis sheds light on the large scale coherent modes and scaling laws of the eigenspectra, we also observe a slow convergence of the spectral trends with the available number of snapshots. Since the finite sized array is periodic in the spanwise direction, we propose to adapt a novel approach of performing POD analysis of the spanwise/lateral Fourier transformed velocity snapshots instead of the snapshots themselves. This methodology not only helps in decoupling the length scales in the spanwise and the streamwise direction when studying the energetic coherent modes, but also provides a detailed guidance towards understanding the convergence of the eigenspectra. In particular, the Fourier-POD eigenspectra helps us illustrate if the dominant scaling laws observed in 3D POD are actually contributed by the laterally wider or thinner structures and provide more detailed insight on the structures themselves. We use the database from our previous large eddy simulation (LES) studies on finite-sized wind farms which uses wall-modeled LES for modeling the Atmospheric boundary layer laws, and actuator lines for the turbine blades. Understanding the behaviour of such structures would not only help better assess reduced order models (ROM) for forecasting the flow and power generation but would also play a vital role in improving the decision making abilities in wind farm optimization algorithms in future. Additionally, this study also provides guidance for better understanding the POD analysis in the turbulence and wind farm community.