In this study, the external magnetic field emitted by a wireless power transfer (WPT) system and the internal electric field induced into human body models during recharging operations of a compact electric vehicle (EV) are evaluated. To this aim an ad-hoc formulation for the source modeling is coupled with a commercial software that performs numerical dosimetry. Specifically, two realistic anatomical models both in a driving position and in a standing posture are considered, and the chassis of the EV is modeled either as a currently employed aluminum alloys and as a futuristic carbon fiber composite panel. Aligned and misaligned coil configurations of the WPT system are considered as well. The analysis of the obtained results shows that the ICNIRP reference levels are exceeded in the driving position, especially for the carbon fiber chassis, whereas no exceedance is observed in terms of basic restrictions, at least for the considered scenarios.

In this work, it is analyzed a Digital Signal Processor, DSP, based One Cycle Control, OCC, strategy for a Power Factor Corrector, PFC, rectifier, which presents Common-mode Voltage, CMV, immunity. The proposed strategy utilizes an emulated-resistance-controller in closed-loop configuration to set up dc-link voltage and achieve unity power factor, UPF. It is shown that if PFC can achieve UPF condition and if phase voltage is only affected by CMV, then phase current is free from CMV, as well as a lead-lag compensator, LLC, to average phase current. Another possible condition is also analyzed. The proposal is verified by simulation and experiments.

We describe a novel machine that uses the greater-than-100% efficiency of air/ground-source heat pumps to recursively generate electricity via steam-powered generators, taking thermal energy from the ambient environment to convert into electrical power. The invention of a machines that generate clean power at low costs will be fundamental to the future of electricity. We estimate efficiency and calculate minimum efficiencies for unknown parts to consider the applicability of this machine to the real world.

Wind power output is highly dependent on the wind speed at the selected site, therefore wind-speed distribution modeling is the most important step in the assessment of wind energy potential. This study aims at accurate evaluation of onshore wind energy potential in seven coastal cities in the south of Iran. Six Probability Distribution Functions (PDFs) were examined over representative stations. It has been deduced that the Weibull function, which was the most used PDF in similar studies, was only applicable to one station. Here, Gamma offered the best fit for three stations and for the other ones, Generalized Extreme Value (GEV) performed better. Considering the ranking of six examined PDFs and the simplicity of Gamma, it was identified as the effective function in the southern coasts of Iran bearing in mind the geographic distribution of stations. Besides, six turbine power curve functions were contributed to investigate the capacity factor. That was very important, as using only one function could cause under- or over-estimation. Then, stations were classified based on the National Renewable Energy Laboratory system. Last but not least, examining a range of wind turbines enabled scholars to extend this study into the practice and prioritize development of stations considering budget limits.

The IEEE 802.15.4-2015 standard includes the SUN (Smart Utility Networks) modulations, i.e., SUN-FSK, SUN-OQPSK and SUN-OFDM, which provide long range communications and allow to trade data rate, occupied bandwidth and reliability. However, given the constraints of low-power devices and the challenges of the wireless channel, communication reliability cannot still meet the PDR (Packet Delivery Ratio) requirements of industrial applications, i.e., PDR>99%. Hence, in this paper we evaluate the benefits of improving communication reliability by combining packet transmissions with modulation diversity using multiple IEEE 802.15.4g SUN modulations. The results derived from a real-world deployment show that going from 1 to 3 packet transmissions with the same SUN modulation can increase PDR from 85.0/84.6/71.3% to 94.2/94.1/86.0% using SUN-FSK, SUN-OQPSK and SUN-OFDM, respectively. Combining the same number of packet transmissions with modulation diversity allows to further increase the average PDR to 97.1%, indicating its potential as a tool to help meeting the reliability requirements of industrial applications.

The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

This paper presents the design of a high frequency zero voltage switching (ZVS) full-bridge converter with a phase-shifted driving signal for photovoltaic applications. The resonant power converter can provide high-power capacity under high-frequency operation. The proposed power converter can also reduce the size of the transformer under the same power rating. The high-frequency transformer was developed by using the resonant and switching frequencies of the power converter to reduce the switching loss and to improve the system efficiency. Phase-shifted modulation was selected to drive the switches of a full-bridge power converter based on the switching loss minimization method. The desired output voltage was controlled using a closed-loop controller under a loop gain stability margin. The simulation results showed that the output voltage can be controlled to the desired constant when the input voltage changes from 30 VDC to 60 VDC. The desired output voltage of power converter is constant at 400 VDC. The power converter can transfer the DC supply to a 220 VAC household via grid-connected inverter. Therefore, the proposed study showed the effectiveness of the phase-shift ZVS full-bridge power converter with high-frequency transformer. This power converter can control the operation of the desired voltage system and has a small sizing of power converter system, low switching loss, and high system efficiency.

We write the mutual information between an input speech utterance and its reconstruction by a Code-Excited Linear Prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short term predictor, the adaptive codebook, and the fixed codebook. We then show that a recently introduced quantity, the log ratio of entropy powers, can be used to estimate these mutual informations in terms of bits/sample. A key result is that for many common distributions and for Gaussian autoregressive processes, the entropy powers in the ratio can be replaced by the corresponding minimum mean squared errors. We provide examples of estimating CELP codec performance using the new results and compare to the performance of the AMR codec and other CELP codecs. Similar to rate distortion theory, this method only needs the input source model and the appropriate distortion measure.

This study aims to determine the optimal configuration (position and operation duration) for wall mounted mechanical mixers based on the comparison of three-dimensional computational fluid dynamics (CFD) modelling results and physical data collected from the treatment plant. A three-dimensional model of anoxic zone 1, 2 and 3 of Northern Wastewater Treatment Plant (WWTP) located at Cairns Regional Council, Cairns, Queensland, Australia was developed and validated. The model was used to simulate the flow pattern of the WWTP and the simulation results are in good agreement with the physical data varying between 0% to 15% in key locations. The anoxic zones were subject to velocities less than the desired 0.3 metres per second however results for suspended solids concentration indicate that good mixing is being achieved. Results for suspended solids concentrations suggest that the anoxic zones are towards the upper limits recommended by literature for specific power dissipation. The duration for operation of mechanical mixers was investigated and identified that the duration could be reduced from 900 seconds down to 150 seconds. Alternative mixer positioning was also investigated and identified positioning which would increase the average flow velocity with decreased duration (150 seconds). The study identified that Council may achieve savings of $24,000 per year through optimisation of the mechanical mixers.

The VLSI industry is facing parasitic effects that trouble development in the nanoscale domain. However, instead of replacing the traditional MOSFET design, it would be more advantageous to apply different doping profiles and discerning which deal with specific parasitic effects the best. With a review of Gaussian doping, Uniform doping, and Delta doping profiles and analysis of the FET technology characteristics that use these doping profiles, a comparison can be made among them for integrated circuit design engineers. These doping profiles are compared based on how well they perform against non-ideal and ideal environments. Also, both digital and analog performance are measured to ensure the uniqueness of each doping profile that is present. After getting a list of benefits from each doping profile, it is derived to determine which doping profile works best against a host of parasitic effects and what type of application do these doping profiles have

This study shows the application of self-organizing maps (SOMs) to probabilistic forecasts of wind power generation and ramps in Japan. SOMs are applied to atmospheric variables obtained from atmospheric reanalysis over the region, thus deriving classified weather patterns (WPs). Probabilistic relationships are established between the synoptic-scale atmospheric variables over East Japan and the generation of regionally integrated wind power in East Japan. Medium-range probabilistic wind power predictions are derived by SOM, as analog ensembles based on the WPs of the multi-center ensemble forecasts. As this analog approach handles stochastic uncertainties effectively, probabilistic wind power forecasts are rapidly generated from a very large number of forecast ensembles. The use of a multi-model ensemble provides better results than a one-forecast model. The hybrid ensemble forecasts further improve the probabilistic predictability skill of wind power generation, as compared with non-hybrid methods. It is expected that long-term wind forecasts will provide better guidance to transmission grid operators. The advantage of this method is that it can include an interpretative analysis of meteorological factors for variations in renewable energy.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the analysis of collected data. This procedure is essentially based on the comparison of lines present in the spectrum with a literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of non-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages) 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (Decision trees, Random forest, k-Nearest Neighbour, Support Vector Machine, Probabilistic Neural Network, Multi-Layer Perceptron, and Generalized Regression Neural Network), 5-fold stratified cross-validation and test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08% with average classification time of about 0.12 s is obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

Visible Light Communication (VLC) uses an intensity-modulation and direct-detection (IM/DD) scheme to transmit data. The light source utilized in VLC structures is constantly switched on and off rapidly, resulting in flickering. Furthermore, most of illumination systems exclude dimming support to permit customers to dim the light source to the desired stage. Therefore, the modulation scheme for information transmission in VLC should consist of flicker mitigation and dimming manage abilities. Double inverse pulse position modulation (DIPPM), sub-carrier pulse position modulation (SC-L-PPM), and M-ary variable pulse position modulation (M-VPPM) are recommended for minimizing the flicker issues and supporting an excessive degree of dimming for VLC systems. In this paper, a comparison is introduced between DIPPM, SC-L-PPM, and M-VPPM according to error performance and bit rate. In addition, a simulation is carried out to measure the optical power distribution for a LED lamp in an indoor room topology for each modulation technique. The obtained results indicated that SC-L-PPM is the best choice regarding the bit error rate (BER) and optical power distribution compared to the other two schemes. A 10^-6 BER is achieved with a very low power requirement at L=8, while a remarkable power distribution of 1.5 to 6.5 dBm is observed from 3×5×5 m³ room corners to the center, respectively. According to the bit rate, it is noticed that, M-VPPM is the most efficient one compared to the two other schemes. It achieves 2.3×10⁷ bps at signal to noise ratio (SNR) of 22 dB and M=8.

The ecological background condition of the semi-arid steppe region (SASR) is extremely fragile. It is recognized that the development of coal and electricity power is a kind of strong human interference behavior for regional landscape ecology. Landscape ecological classification (LEC) is the premise of landscape ecology research of the mining area. The current research on the SASR and grassland LEC of coal-power base is relatively less, but still remains uncertainty concerning how to stratify and classify urban mining landscapes into units of ecological significance at spatial scales appropriate for management. This study is based on hierarchy theory, scale theory, landscape process, the patch-corridor-matrix model, the network, the theory of multiple planning integration and the principle of remote sensing. According to the comprehensive principle, principles of the combining of structure and function, principle of the combining human-ominated and natural landscape, principle of emphasis, and principle of combining qualitative analysis with quantitative research of LEC in large-scale coal-power base(LSCPB). On the basis of occurrence method land classification, fully consider the ecological attributes of the land, integration pattern, processes and function theory of the landscape ecology, the LEC system of the LSCPB in the SASR has been constructed by using top-down decomposition classification method. Empirical research of the Victory and Mindong No.1 mining areas of Shenhua Group shows that the classification system constructed in this paper can meet the requirements of LEC and fully reflect the status of landscape ecology of LSCPB in SASR. This study can provide theoretical guidance for the landscape ecology of LSCPB, while also supporting a theoretical reference for the LEC research.

Three-phase to single-phase matrix converter (TSMC) is used as the transmitter for transient electromagnetic method (TEM) instrument, instead of an AC-DC converter and an H-bridge inverter. The transmitter produces excitation current for primary field. The output current has a waveform with a bipolar square of half duty cycle, which is required by TEM. The space vector PWM based on input currents is discussed in detail. The control method of input power factor is proposed for TSMC. The simulation model of TSMC is established and the simulations of different conditions are completed. The simulation results show the controllability of the input power factor is illustrated.

In this paper, an extensive study on the operation, control and performance of a hybrid photovoltaic and wind turbine (PV-WT) system is presented. The study includes the operation of the hybrid systems under different weather conditions which are reflected on a change in the wind speed and irradiance. Various loading conditions are investigated such a change in active and reactive power demands for static and dynamic loads. The performance of the hybrid system is examined via MATLAB/SIMULINK simulations.

A programmable DC power supply with Real-time Digital Simulator (RTDS)-based photovoltaic (PV) Power Hardware-In-the-Loop (PHIL) simulators have been used to improve the control algorithm and reliability of PV Inverter. This paper proposes a supervisory control algorithm for PV PHIL simulator with non-RTDS device that is an alternative solution of high cost PHIL simulator. However, when such a simulator with conventional algorithm which is used in RTDS is connected to a PV inverter, the output is in the transient state and it makes it impossible to evaluate the performance of the PV Inverter. Therefore proposed algorithm controls the voltage and current target values according to the constant voltage (CV) and constant current (CC) modes to overcome the limitation of the Computing Unit, DC power supply and also uses a multi-rate system to account for the characteristics of each component of simulator. A mathematical models of a PV system, programmable DC power supply, isolated DC measurement device and Computing Unit are integrated to form a real-time processing simulator. Performance tests using a PV PHIL simulator which is applied proposed algorithm connected a PV inverter are carried out and proved superiority and utility of this method against conventional methods.

This paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed multi-objective optimization model optimizes not only the operating cost, including fuel cost, purchasing cost, battery degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation from the set point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, the detailed thermal dynamic model of buildings is integrated into the distribution optimal power flow (D-OPF) model for the optimal operation. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of the proposed model and significant savings in electricity cost with network operational constraints satisfied.

Efficiently solving the problem of computing, in a distributed fashion, the shortest paths of a graph whose topology dynamically changes over time is a core functionality of many today’s digital infrastructures, probably the most prominent example being communication networks. Many solutions have been proposed over the years for this problem that can be broadly classified into two categories, namely Distance-Vector and Link-State algorithms. Distance-Vector algorithms are widely adopted solutions when scalability and reliability are key issues or when nodes have either limited hardware resources, as they result in being very competitive approaches in terms of both the memory and the computational point of view. In this paper, we first survey some of the most established solutions of the Distance-Vector category. Then, we discuss some recent algorithmic developments in this area. Finally, we propose a new experimental study, conducted on a prominent category of network instances, namely generalized linear preference (GLP) power-law networks, to rank the performance of such solutions.

This paper deals with a generic methodology to evaluate the magnetic parameters of contactless power transfer systems. Neumann's integral has been used to create a matrix method that can model the magnetics of single coils (circle, square, rectangle). The principle of superposition has been utilised to extend the theory to multi-coil geometries such as double circular, double rectangle and double rectangle quadrature assuming linearity of magnetics. Numerical and experimental validation has been performed to validate the analytical models developed. A rigorous application of the analysis has been carried out to study misalignment and hence the efficacy of various geometries to misalignment tolerance. Comparison of single-coil and multi-coil shapes considering coupling variation with misalignment, power transferred and maximum efficiency is carried out.

Micro plasma devices (MPD) with power gains are of interest in applications involving operations in the presence of ionizing radiations, in propulsion, in control, amplification of high power electromagnetic waves, and in metamaterials for energy management. Here we review and discuss MPDs with an emphasis on new architectures that have evolved during the past 7 years. Devices with programmable impact ionization rates and programmable boundaries are developed to control the plasma ignition voltage and current to achieve power gain. Plasma devices with 1-10 μm gaps are shown to operate in the sub-Paschen regime in atmospheric pressures where ion-assisted field emission results in a breakdown voltage that linearly depends on the gap distance in contrast to the exponential dependence dictated by the Paschen curve. Small gap devices offer higher operation frequencies at low operation voltages with applications in metamaterial skins for energy management and in harsh environment inside nuclear reactors and in space. In addition to analog plasma devices, logic gates, digital circuits, and distributed amplifiers are also discussed.

This paper presents a new optical, multi-functional, high-resolution 3-axis sensor which serves to navigate and can, for example, replace standard joysticks in medical devices such as electric wheelchairs, surgical robots or medical diagnosis devices. A light source, e.g. a laser diode is affixed to a movable axis and projects a random geometric shape on an image sensor (CMOS or CCD). The software in the downstream microcontroller identifies the geometric shape’s center, distortion and size, then calculates X, Y, and Z coordinates. These coordinates can then be processed in attached devices. The 3-axis sensor is characterized by its very high resolution, precise reproducibility and plausibility of the coordinates produced. In addition, optical processing of the signal provides a high level of safety against electromagnetic and radio frequency interference. The sensor presented here is adaptive and can be adjusted to fit a user’s range of motion (stroke and force). This recommendation aims to optimize sensor systems such as joysticks in medical devices in terms of safety, ease of use, and adaptability.

Extremely low-frequency electromagnetic fields (ELF-EMFs) are non-ionizing radiations typically emitted by power lines, electrical wiring and electrical appliances. Epidemiological studies have repeatedly shown a positive association between ELF-EMFs and childhood leukemia. Exposures greater than 0.3-0.4 µT increase the risk by approximately 1.5-2 fold, and estimates indicate that up to 2% of childhood leukemia cases in Europe may be attributable to ELF-EMFs. However, it is considered unlikely that ELF-EMFs can cause cancer, because carcinogenesis requires the accumulation of DNA alterations and ELF-EMFs do not have enough energy to damage the DNA. Lack of biological plausibility is a barrier to accept the evidence of carcinogenicity in human studies and to take measures to protect pregnant women and children from ELF-EMFs. Recent evidence indicates that non-mutagenic agents can cause DNA alterations and increase the risk of cancer by promoting the accumulation of cell divisions in stem cells. Cell division generates DNA alterations (e.g., mutations arising during DNA replication), which occur even in the absence of DNA-damaging agents. Importantly, ELF-EMFs can trigger the division of stem cells; this effect is under development in the field of regenerative medicine. A possible mechanism by which ELF-EMFs induce the malignant transformation of hematopoietic stem cells is discussed.

In the context of the Korean building structure standard KDS2022, wind loads can be readily calculated in terms of the along-wind, across-wind, and torsional components, based on a single wind direction. However, this approach falls short in accurately and safely evaluating the impact of winds from various directions. Therefore, in this study, we developed a Wind Load Calculation Program (WCP) capable of predicting wind loads with relative precision during the preliminary design phase. First, wind tunnel tests were conducted to identify the essential factors necessary for calculating wind loads and the variables influencing these factors. The building shape considered was a square, and the wind force coefficients and power spectral density were measured by combining 4 ground roughness values, 11 side ratios (), 4 aspect ratios (), and wind directions ranging from 0° to 90°. Subsequently, a generalized formulation was devised to facilitate wind load calculations across diverse conditions and a method for calculating load combination coefficients, considering response correlation and load simultaneity, was presented. Finally, the wind loads obtained from the wind tunnel tests were compared with those predicted by the WCP using an actual project model.

Progress in laser-driven proton acceleration requires increasing the proton maximum energy and laser-to-proton conversion efficiency, while reducing the divergence of the proton beam. However, achieving all these qualities simultaneously has proven challenging experimentally, the increase in beam energy often coming at the cost of beam quality. Numerical simulations suggest that coupling multi-PW laser pulses with ultrathin foils could offer a route for such simultaneous improvement. Yet experimental investigations have been limited by the scarcity of such lasers and the need for very stringent temporal contrast conditions to prevent premature target expansion before the pulse maximum. Here, combining the newly commissioned Apollon laser facility that delivers high-power ultrashort (∼24fs) pulses, with a double plasma mirror scheme to enhance its temporal contrast, we demonstrate the generation of up to 35 MeV protons with only 5 J of laser energy. This approach also achieves improved laser-to-proton energy conversion efficiency, reduced beam divergence, and optimized spatial beam profile. Therefore, despite the laser energy losses induced by the plasma mirror, the proton beams produced by this method are enhanced on all accounts compared to those obtained under standard conditions. Particle-in-cell simulations reveal that this improvement results from the intensified electrostatic field caused by a more compact hot-electron distribution.

There are important challenges for modeling large electrical distribution circuits, even more with the presence of distributed renewable generation. Constructing simulations for assessing the effect of the penetration of distributed generation on electrical distribution networks has become of great importance for Distribution Network Operators (DNOs). This paper proposes a simulation strategy based on open-source platforms and the integration of scripting tools for the rapid modeling of large-scale electrical distribution circuits with distributed renewable generation. The implementation is based on the adaptation of a tool called QGIS2OpenDSS, which creates OpenDSS distribution network models directly from an open-source geographic information system, QGIS. The plugin’s capabilities are demonstrated using a real distribution feeder with more than 60% penetration of renewable generation based on photovoltaic systems. These simulations are carried out using real data from a circuit provided by a DNO in the Dominican Republic, which is used to demonstrate how this approach provides a more accessible and flexible way to simulate and assess the effect of Distributed Energy Resources (DERs) in Medium Voltage (MV) and Low Voltage (LV) networks, enabling utilities to evaluate system performance and identify potential issues.

Recent advancements in semiconductor photonic and electronic devices such as Uni-travelling-carrier photodiodes (UTC-PDs), have played a significant role in the development of Terahertz communication technology. However, a major challenge that still persists is the limited output power from a single UTC-PD required for practical transmission distances. To enhance the output power, we propose and demonstrate a novel Terahertz wave power combining technique using a photomixer device that comprises of two arrayed UTC-PDs monolithically integrated with a microstrip line-based 2 × 1 Wilkinson power combiner and a rectangular patch antenna on a Silicon Carbide (SiC) substrate at 300 GHz. At photocurrents of 8 mA and 10 mA from the two UTC-PDs, the device experimentally exhibited a 7.3 dB increase in power relative to the power obtained with 8 mA UTC-PD and a 4.4 dB increase in power relative to the power obtained with 10 mA UTC-PD, when both UTC-PDs were irradiated. This implies that power can be enhanced by a factor of N2 if the photocurrent is multiplied by a factor of N. This technology presents a holistic and cutting edge platform for further power multiplication by further increasing the number of arrayed UTC-PDs.

In this paper, the single event burnout(SEB) and reinforcement structure of 1200V SiC MOSFET (SG-SBD-MOSFET) with split gate and schottky barrier diode(SBD) embedded were studied. The device structure was established using Sentaurus TCAD, and the transient current changes of single event effect(SEE), SEB threshold voltage, as well as the regularity of electricfield peak distribution transfer were studied when heavy ions were incident from different regions of the device. Based on SEE analysis of new structural device, two reinforcement structure designs for SEB resistance were studied, namely the expansion of P+body contact area and the design of multi-layer N-type interval buffer layer. Firstly, two reinforcement schemes for SEB were analyzed separately, and then comprehensive design and analysis were carried out. The results showed that the SEB threshold voltage of heavy ions incident from the N+source region was increased by 16% when using the P+body contact area extension alone; When the device is reinforced with multi-layer N-type interval buffer layer alone, the SEB threshold voltage increases by 29%; The comprehensive use of P+body contact area expansion and multi-layer N-type interval buffer layer reinforcement increased the SEB threshold voltage by 33%. Overall, the breakdown voltage of the reinforced device decreased from 1632.935V to 1403.135V, which can be seen as reducing the remaining redundant voltage to 19%. The device performance was not significantly affected.

This paper investigates an innovative photovoltaic cell configuration, denoted as “Au/CuSCN/RbGeI3/CdTe/ZnO/TiO2/FTO", using SCAPS-1D simulation. The research focuses on optimizing device performance by analyzing key components, including ZnO as the electron transport layer (ETL), RbGeI3 as the perovskite absorber layer, CuSCN as the hole transport layer (HTL), and CdTe as the active buffer layer. A comparison is made with the standard “Au/CuSCN/CH3NH3SnI3/CdTe/ZnO/TiO2/FTO" configuration under dark and illuminated conditions. Through an in-depth investigation involving temperature effects, generation rate analysis, RbGeI3 absorber layer thickness, Nd level, and interface defect levels, an optimized design for a unit area is achieved. The proposed solar cell demonstrates outstanding performance metrics, including an open circuit voltage (Voc) of 0.94V, a short circuit current density (Jsc) of 34.52mA/cm2, a fill factor (FF) of 77.77%, and an energy conversion efficiency (η) of 25.40%. These values significantly outperform the reference models with energy conversion efficiencies of 10.11% and 21.09%. Real-world applicability is validated by implementing these optimized characteristics in a photovoltaic system for Pakistan’s geographical location using PVSyst software. The suggested solar cell achieves a remarkable performance ratio (PR) of 85.6%, surpassing the PR of the reference basic solar cell model at 83%. The findings of this research offer a promising and superior solar cell design, incorporating innovative materials and optimization techniques to achieve exceptional energy conversion efficiency and overall performance. This study’s outcomes have the potential to advance photovoltaic technology and contribute to sustainable energy solutions worldwide.

In this paper, a continuous power management for a decentralized DC microgrid (DCMG) is proposed to achieve voltage regulation and power balance even under system uncertainty and voltage sensor failure. The DCMG system achieves a continuous power management through only the primary controller to reduce the computational burden of each power agent. To enhance the reliability and resilience of the DCMG system under voltage sensor failure, a DC bus voltage (DCV) sensor fault detection algorithm is proposed. In this algorithm, the DCV sensor failure is detected by comparing the measured DCV with the estimated DCV. When the power agent identifies the DCV sensor failure, it changes the operation properly according to the proposed control mode decision algorithm to maintain the stability of the DCMG system. When uncertain conditions such as the power variation of the distributed generations, sudden grid disconnection, DCV sensor failure, electricity price change, and critical battery status occur, the DCMG system is changed to transitional operation modes. These transitional operation modes are employed to transmit the power agent information to other agents without digital communication links (DCLs) and to accomplish power sharing even under such uncertain conditions. In the transitional operation modes of the DCMG system, the DCV levels are temporarily shifted to an appropriate level, enabling each power agent to detect the uncertainty conditions, and subsequently to determine its operation modes based on the DCV levels. Simulation and experimental results under different test conditions confirm the reliability and effectiveness of the proposed control scheme.

{This study investigates the coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence criteria based on real biological group experiments, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled force Cucker-Smale model with informed leaders. The inclusion of the informed leader aspect distinctively characterizes the dynamics of leaders and followers, enabling a more accurate representation of biological group behavior. The paper then proposes the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. By employing time slicing and rolling time windows, the study methodically analyses group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network's superior performance. The Kolmogorov-Smirnov test indicates that the FLH network follows a power-law distribution, highlighting the evolutionary intelligence of biological groups. This research offers new perspectives on the evolution of biological groups, contributing to our understanding of both natural and artificial systems, such as animal migration and autonomous drone operations.

In recent years, there has been an increasing interest in adopting novel sensing technologies for the continuous monitoring of structural systems. In this respect, Micro-Electro-Mechanical System (MEMS) sensors are widely used in several applications, including Structural Health Monitoring (SHM). Thanks to their significantly lower cost, ease of installation on the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analysis show that MEMS can correctly detect modal frequencies, useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

The capability of in-house transient wave-multibody computational tool of ITU-WAVE are extended to predict the wave power absorption with Wave Energy Converters (WECs) arrays placed in front of a vertical breakwater. The analyses of hydrodynamic exciting and radiation forces are approximated solving boundary integral equation at each time interval. The reflection of incoming waves due to a vertical wall is predicted with method of images. The constructive or destructive performance of WECs arrays with different array configurations is measured with mean interaction factor. The behaviour of the exciting and radiation hydrodynamic forces of each WEC due to a vertical wall effect shows considerable differences than those of WECs arrays without a vertical wall influence. When the wave power absorption with WECs arrays with and without a vertical wall effect are compared, it is shown with numerical experiences that WECs placed in front of a vertical wall have much greater effects on wave power absorption. This can be attribute to the hydrodynamic interaction, standing waves, and nearly trapped waves in the gap between a vertical wall and WECs arrays. The analytical and other numerical results are used to for the validation of the numerical results of the present ITU-WAVE computational tool for exciting and radiation forces, and mean interaction factor of WECs arrays which show satisfactory agreements.

a sensorless control scheme based on novel power converging law sliding mode observer is suggested to realize the sensorless control for the permanent magnet synchronous motor (PMSM). Firstly, by introducing a new power converging law, the problem of slow converging speed/large chattering value dilemma in the conventional converging law is solved; Secondly, in view of the shortcomings of the conventional position and speed estimation algorithm, the high-frequency buffeting in the sliding mode observer will be expanded, a phase-locked loop (PLL) velocity and position estimation strategy is adopted to replace it. Finally, through Matlab/Simulink modeling and motor speed regulation platform experimental results, the sensorless position and speed estimation technology proposed in this paper can respond to the starting speed command of 1000 r/min within 0.05 s, and the starting process has no overshoot. When disturbed by 10Nm loading, the speed fluctuation value can be controlled within 1%.

Environmental responsibility and economic benefits have promoted the development of closed-loop supply chain (CLSC), and shortages and channels are considered to be two important issues in CLSC. This paper explores ordering and channel decisions in CLSC with new and differentiated remanufactured products, and considers the price and sales effort-dependent demands, as well as the proportion of emergency orders determined by emergency order costs and backorder losses, and establishes an integrated and decentralized CLSC game models. We introduce a stochastic sales effort, which affects two types of products. Numerical results show that sales effort and the order quantity of new and remanufactured products exhibit concave and convex functions, respectively. Wherein, the upper limit of sales effort has a greater impact on supply chain decisions. High sales efforts can serve as a means of coordinating dispersed supply chains. Moreover, in different cases, decisions of integrated channel are better than those of decentralized channel. Finally, whether the supply chain adopts an emergency order strategy depends on the relative cost of emergency orders and out of stock costs. According to the research in this article, some management insights are also provided.

This study delves into the complex dynamics of Underwater Wireless Optical Communication (UWOC) in varied Harbor water conditions. Focusing on four distinct types of Harbor water, the research employs the Monte Carlo method to simulate photon transmission and analyze critical UWOC characteristics. Key parameters such as the transmitted full divergence angle, received aperture, and Field of View (FOV) are meticulously evaluated for their impact on communication efficacy. The study first explores the relationship between laser power variation, attenuation length, and divergence angle, revealing how different water qualities influence received power and time delay spread. Notably, the received normalized power loss and time delay spread are found to be more significantly affected by communication distance than water quality. In addition, under the condition of small received aperture and FOV, the influence on normalized power loss and time spread is more obvious in Harbor water. The findings offer novel insights into the challenges of UWOC in Harbor waters, particularly in terms of light scattering and absorption properties, and contribute to the optimization of communication systems in such complex underwater environments. Furthermore, the analysis of time pulse spread in turbid water quality is of great significance for the analysis and improvement of underwater wireless optical timing, ranging and positioning.

Gas turbine engines are widely used in various industries such as aviation, power generation, and marine propulsion due to their high efficiency and reliability. The combustion chamber of a gas turbine engine design contains a number of elements, including as the chamber's size and form, the fuel injection system, the ignition system, and the cooling system and it plays a critical role in the performance and efficiency of the engine. This research discusses the fabrication of a combustion chamber model for a gas turbine engine. The aim of this research is to develop a combustion chamber that is cost-effective, easily available, and can be fabricated in less time. The research covers the different types of combustion chambers used in gas turbine engines and compares their advantages and disadvantages. It then selects the best type of combustion chamber for the gas turbine engine based on its cost, availability, and fabrication time, Efficiency and high power to fuel ratio. The report also describes the fabrication process involved, including the techniques and materials used, along with safety precautions and environmental protection measures. Lastly, the research suggests future improvements and potential applications for the fabricated combustion chamber model.

This paper presents a Modified Gate Diffusion Input (MGDI) design with four inputs for Quantum-dot Cellular Automata (QCA) technology. QCA is a promising nanotechnology that offers benefits such as low power consumption, and high density, making it a potential alternative to CMOS technology. The proposed planar cell design has advantages over previous designs, including a 19% reduction in the number of cells, a 29% reduction in total energy consumption, and a 44% reduction in average energy loss at the same occupancy level. The proposed block is capable of implementing 21 basic combinational functions and various sequential functions. The simulation of functions in 18nm technology and energy consumption calculation indicates that the resulting circuits have improvements of 41%, 27%, 27%, and 20% compared to multiplexers, comparators, four-bit parity circuits, and single-bit memory, respectively. Additionally, the number of cells in the proposed circuits has improved by 10%, 7%, 22%, and 19%, respectively.

This paper proposes a circuit model of Cross-Coupled CMOS AC – DC Charge Pump (XC – CP) operating in subthreshold region. The aim is improving the efficiency to design XC – CPs with variety of specifications, e.g., input and output voltages and AC input frequency. First, it is shown that the output resistance (Ro) of XC – CP is much higher than those of CPs with single diodes (SD – CP) and ultra-low-power diodes (ULPD – CP) as charge transfer switches (CTSs). Second, the reason behind the above feature of XC – CP is identified by a simple model that the gate-to-source voltages of CTS MOSFETs are independent of the output voltage of the CP. Third, high but finite Ro of XC – CP is explainable with a more accurate model that includes the dependence of the saturation current of MOSFETs operating in subthreshold region on the drain-to-source voltage which is a function of the output voltage of CP. The model was in good agreement with measured and simulated results of XC – , SD – and ULPD – CPs fabricated in 250 nm CMOS.

Under the "dual carbon" target in China, virtual power plants (VPPs) play an important role in improving grid security and promoting clean and low-carbon energy transformation. VPPs can integrate and control distributed resources to participate in the energy market and make full use of distributed resources. However, the intermittency and volatility of renewable energy and the "heat-driven" working mode of CHP units create contradictions that seriously affect the peak-shaving ability of VPPs and lead to high carbon emissions. To solve these problems, this paper aggregates CHP units, wind power, photovoltaics, carbon capture, hydrogen energy storage, and electric boilers into a new type of virtual power plant. The "hydrogen energy storage-electric boiler" joint decoupling CHP working mode is used to strengthen the coupling relationship between electric-thermal-hydrogen load. At the same time, a tiered carbon trading mechanism is considered, with the net profit of the VPP as the optimization objective, balancing economic and environmental considerations. A low-carbon economic dispatch model for VPPs is established, and a genetic algorithm is used for optimization. Three different scheduling strategies are set, and simulations are conducted in three different seasonal scenarios. The results show that the net profit in the cooling season increased by 50.4%, and carbon emissions decreased by 42.3%. In the transitional season, the net profit increased by 39.2%, and carbon emissions decreased by 44.9%. In the heating season, the net profit increased by 19.4%, and carbon emissions decreased by 43.4%. Overall, the proposed dispatch strategy can improve the new energy consumption capacity and total revenue of VPPs while achieving the goal of reducing carbon emissions.

We examine a linear q-difference differential equation which is singular in complex time t at the origin. Its coefficients are polynomial in time and bounded holomorphic on horizontal strips in one complex space variable. The equation under study represents a q-analog of a singular partial differential equation, recently investigated by the author, which comprises Fuchsian operators and entails a forcing term that combines polynomial and logarithmic type functions in time. A sectorial holomorphic solution to the equation is constructed as a double complete Laplace transform in both time t and its complex logarithm log t and Fourier inverse integral in space. For a particular choice of the forcing term, this solution turns out to solve some specific nonlinear q-difference differential equation with polynomial coefficients in some positive rational power of t. Asymptotic expansions of the solution relatively to time t are investigated. A Gevrey type expansion is exhibited in a logarithmic scale. Furthermore, a formal asymptotic expansion in power scale is displayed, revealing a new fine structure involving remainders with both Gevrey and q-Gevrey type growth.

A full potential all-electron density functional method within generalized gradient approximation is used to investigate the electronic, elastic and transport properties of cubic copper sulphide and copper selenide. The electronic structure suggests metallic behavior with a zero energy band gap for both materials. Elastic property calculations suggest a stiff material with the bulk to shear modulus ratio of 0.35 and 0.44 for Cu2S and Cu2Se respectively. The transport properties were estimated using the Boltzmann transport approach. Seebeck coefficient, electrical conductivity, thermal conductivity, and power factor all suggest a potential p-type conductivity for Cu2S and n-type conductivity for Cu2Se.

Generally, when cognitive radio users are in Underlay mode, through the analysis of the electromagnetic spectrum in time, frequency, space, and field of multiple dimensional data accounts for judging the inherent correlation of the radio spectrum used/idle state, this is the realization of cognitive radio users (secondary users, SUs) efficient access to the foundation of the limited spectrum resources. Therefore, how to efficiently use of spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper addressing the problem. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results show that our model achieved better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirmed the feasibility of the model.

The decentralized treatment of wastes is studied for an urban scenario considering a high-density population of 2500 inhab./km2. Food and garden waste were assumed to be treated in a co-digestion configuration using several mid-size digesters. In contrast biogas and digestate valorization were carried out in a centralized manner. Electricity and thermal energy were produced from this configuration, accounting for 1.3% of residential electricity demand and 3.2% of thermal demand when using double-turbocharged engines. However, the location of the treatment plants is a factor that may raise social discomfort. Rejection of locating the plant in specific sites close to residential neighbors, nuisance due to possible odor and gaseous emissions, and house market distortions are just a few of a long list of problematic aspects that may threaten the decentralized alternative. These factors are of great relevance and must be given a practical solution if the circular economic model is to be implemented by considering the insertion of waste streams into the production system and generating local energy sources and raw materials.

The increased adoption of information and communication technology for smart grid applications will require innovative cyber-physical system (CPS) testbeds to support research and education in the field. The groundbreaking CPS testbeds with realistic and scalable platforms have progressively gained interest in recent years, with electric power flowing in the physical layer and information flowing in the network layer. However, CPSs are critical infrastructures and not designed for testing or direct training, as any misbehaving in an actual system operation could cause a catastrophic impact on its operation. Based on that, it is not easy to efficiently train professionals in CPSs. Aiming to support the advancement and encourage the training of industry professionals, this paper proposes and develops a complete testbed with commercial tools. The testbed can reliably replicate the performance of smart grid systems and the main potential cyber threats that electric grids may face. The complex interdependencies between the cyber and physical domains are discussed in detail, and different case scenarios are presented, providing insightful guidelines for key features and design decisions for future smart grid testbeds.

Digital-based differential amplifiers (DDA) are particularly suitable to low voltage digital integrated circuit technologies. This paper presents an exhaustive analysis of the digital-based analog amplifiers to take advantage of today's high-performance digital technologies and of computer-aided design (CAD) commonly employed to design integrated circuits. The operating principle and the main mathematical relations of the digital-based differential amplifiers are discussed along with an exhaustive explanation of its operating regions and of the corresponding power consumption. Finally, a detailed description of the design procedure of in UMC 180nm standard CMOS technology is shown.

The requirement for integration of power plants due to the cyclical rise in electrical energy consumption is due to fluctuating load demand with the current grid systems. This integration necessitates effective allocating loads to the power plants for a minimum grid-tied transmission line cost while meeting network constraints. In this paper, we formulate an optimisation problem of minimising the total operational cost of all committed plants transmitted to the grid while meeting network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed Particle Swarm Optimization (PSO) method resolve the optimisation problem using piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction with connected load of 850MW, 1263MW and 2630MW power demand are contrasted with previous PSO application cost values, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), (73.86%, 89.10) respectively, and significant environmental-benefit. The proposed co-optimisation approach can enhance a significant self-consumption ratio compared to the baseline method.

In the field of embedded systems, energy efficiency is a critical requirement, particularly for battery-powered devices. RISC-V processors have gained popularity due to their flexibility and open-source nature, making them an attractive choice for embedded applications. However, not all RISC-V processors are equally energy-efficient, and it is important to evaluate their performance in specific use cases. This paper evaluates the energy consumption and resource utilization of a new RISC-V processor, RisCO2, and four existing processors - Zero-riscy, Micro-riscy, Ri5cy, and CV32E40P - in a signal demodulation application for NDIR CO2 sensors. The processors were implemented in the PULPino SoC and synthesized using Vivado IDE. The processor named RisCO2 is based on the RV32E_Zfinx instruction set and was designed from scratch by the authors specifically for low-power signal processing applications such as signal demodulation in CO2 NDIR sensors. The other processors are Ri5cy, Micro-riscy, and Zero-riscy, developed by the PULP Platform team, and CV32E40P (derived from Ri5cy) from the OpenHW Group, all of them widely used in the RISC-V community. Our experiments showed that RisCO2 had the lowest energy consumption among the five processors, with a 53.5% reduction in energy consumption compared to CV32E40P and a 94.8% reduction compared to Micro-riscy. Additionally, RisCO2 had the lowest FPGA resource utilization compared to the best-performing processors, CV32E40P and Ri5cy, with a 46.1% and a 59% reduction in LUTs, respectively. Our findings suggest that RisCO2 is a highly energy-efficient RISC-V processor for NDIR CO2 sensors that require signal demodulation. The results also highlight the importance of evaluating processors in specific use cases to identify the most energy-efficient option. This paper provides valuable insights for designers of energy-efficient embedded systems using RISC-V processors.

Nowadays, energy decarbonization due to the integration of renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical systems networks, especially, in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method improving the low pass filter and moving average for grid-connected photovoltaic system, the novel method includes state-of-charge monitoring control of the supercapacitors energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. The ultimate goal of this research is to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration.

Plant roots have significant effects on the soil erosion rates, since they can strongly change the soil detachment capacity (Dc). This study quantifies Dc at different flow rates in soils with Alnus subcordata species, compared to three other species (Brachythecium plumose, Gleditsia caspica and Sambucus ebulus species) in the Saravan forest park and develops regression equations for predicting Dc. Undisturbed samples collected from soils with the four tree species and subjected to five slopes (from 4.3 to 38.3%) and five water discharges (from 0.28 to 0.71 l m−1 s−1) using a hydraulic flume. The results showed that Dc was significantly lower in soils with Alnus subcordata species compared to Brachythecium plumose, Gleditsia caspica and Sambucus ebulus species, as the consequence of the changes in the root characteristics, so that Dc was negatively correlated with root weight density, root length and root biomass. The unit stream power had high accuracy for predicting Dc for all of the studied species. The lowest value of rill erodibility (Kr) was obtained in soils with Alnus subcordata species using regression relationship between the Dc and the shear stress of the soil. This experiment helped to show the importance of plant roots in reducing the soil detachment rates and provided a contribution in understanding the choice of appropriate species for soil conservation.

The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

Indonesia's SUMBAGUT 150 kV transmission of High Voltage Alternating Current Network (HVAC) system has considerable power losses. These power losses are a critical problem in the transmission network system. Meanwhile, this study provides one solution to reduce power losses using a High Voltage Direct Current (HVDC) network system. Determining the location to convert HVAC into HVDC is very important. The authors use Particle Swarm Optimization (PSO) to get the optimal location on the 150 kV SUMBAGUT HVAC transmission network system. The study results showed that before using the HVDC network system, the power losses were 122.26 MW. Meanwhile, power losses with one transmission HVDC in the "Paya Pasir-Sei Rotan" are 84.16 MW, "Porsa-P. Siantar" 90.83 MW, "Paya Pasir-Paya Geli" 104.14 MW. Then power losses with two transmission HVDC in "Paya Pasir-Sei Rattan" and "Porsa-P. Siantar" is 71.24 MW, "Paya Pasir-Sei Rotan" and "Paya Pasir-Paya Geli" 77.46 MW, "Porsa-P. Siantar" and "Paya Pasir-Paya Geli" 78.52 MW. The last result, power losses with three transmission HVDC in "Paya Pasir-Sei Rotan," "Porsa-P. Siantar," and "Paya Pasir-Paya Geli" lost 64.57 MW.

A quantitative description of the second law of thermodynamics in small scale systems and over short time scales comes from various fluctuation theorems. The applicability of the transient fluctuation theorem in particular to small scale systems perturbed from an initial equilibrium steady-state distribution has been demonstrated both theoretically and experimentally in several works over the past few decades. In addition, some experimental works in the past have also made successful attempts to demonstrate the applicability of the fluctuation theorem to small scale systems evolving from a certain nonequilibrium steady-state distribution over relatively long time scales. To this end, this paper seeks to demonstrate the transient fluctuation theorem for a Brownian particle confined within a power-law trapping potential by following the trajectory of the particle that itself is translating linearly along one dimension with constant acceleration in a viscous fluid. Considered herein is an idealized version of this model, in that it is assumed that the force of the trapping potential is only felt by the translating Brownian particle confined within the trap, and that this Brownian particle moves relative to the fluid molecules that are held stationary. The results presented herein show that the transient fluctuation theorem applies not only to equilibrium steady-state distributions but also to nonequilibrium steady-state distributions of an ideal colloidal system in an accelerated frame of reference in the asymptotic (long-time) limit.

Regression models are mostly used in all fields of sciences for modelling the relationship between a dependent variable and independent variable(s). The least square method is often used to estimate the parameters in a linear model because it is the best linear unbiased estimator. These estimates can only be reliable if the assumption of normality is satisfied. In some cases the dependent variable might be bimodal and shows a non-linear relationship with the independent variable(s). In this case, a non-linear model should be used. In non-linear model, the standard errors are often obtained by linearizing the nonlinear function around the parameter, assuming central limit theorem. After the linearization, the least square parameter estimates are obtained. It should be noted that the error of the non-linear model is different from that of the transformed linear model. Thus, there is a need to transform back to the original non-linear model. In this note, a novel non-linear function was developed into a non-linear regression model, called Normal-Power model. The least square method was used to estimate the parameter of the transformed model. Its usefulness in regression model was demonstrated using real data of Nigeria Economy-Tourism model.

Extensive in-situ resource utilisation (ISRU) will be essential to enable long-duration stays on Luna and Mars and reduce reliance on resupply from Earth. Early development of ISRU technologies has focused on standalone capabilities for specific targets related to life support and ascent propellant. An unexplored opportunity remains for greatly expanding the scope of materials that can be supplied by ISRU, and for integrating various technology platforms into a larger system. Recent advances in power-to-X technology aimed at decarbonising the global economy have made it possible to drive key chemical processes using electricity with small-scale, modular reactor. This paper proposes a vision for an integrated system of ISRU processes based on power-to-X technology to produce oxygen, hydrogen, water, methane, polymers, metal alloys, and synthetic fertilisers, using Martian regolith, atmosphere, and ice. A ‘building block’ strategy is adopted to convert raw materials into versatile intermediaries, which can then be combined to form essential products. A wider range of raw materials are available on Mars compared to Luna, suggesting greater opportunity for ISRU deployment to compensate for the greater time and cost requirements for a Mars resupply mission.

Fiber laser technology has been demonstrated as a versatile and reliable approach for laser source manufacturing with a wide range of applicability in various fields ranging from science to industry. The power/energy scaling of single fiber laser systems has faced several fundamental limitations. To overcome them and to boost the power/energy level even further, combining the output powers of multiple lasers has become the primary approach. Among various combining techniques, the coherent beam combining of fiber amplification channels is the most promising approach, instrumenting ultra-high power/energy lasers with near-diffraction-limited beam quality. This paper provides a comprehensive review of the progress of coherent beam combining for both continuous-wave and ultrafast fiber lasers. The concept of coherent beam combining from basic notions to specific details of methods, requirements, and challenges are discussed, along with reporting some practical architectures for both continuous and ultrafast fiber lasers.

The total amount of greenhouse gas emissions directly or indirectly generated by thermal power enterprises at any given time can be offset through afforestation, energy conservation and emission reduction. The present situation and control methods of CO₂ emission in China's coal-fired thermal power industry are introduced. The complex ecosystem is a unity of ecological functions composed of human society, economic activities and natural conditions. In the context of carbon neutrality and based on the theory of composite ecosystem, this paper divides the coal-fired thermal power plants in China into environmental management zones, calculates the atmospheric environmental capacity, and puts forward the concept of regional atmospheric environmental capacity, classification and zoning control. Finally, the management and control units are classified, and differentiated management and control requirements are put forward to provide a reference for regional air quality standard planning.

As a consequence of the increasing share of renewable energies and sector coupling technologies, new approaches are needed for the study, planning, and control of modern energy systems. Such new structures may add extra stress to the electric grid, as is the case with heat pumps and electrical vehicles. Therefore, the optimal performance of the system must be estimated considering the constraints imposed by the different sectors. In this research, a dispatch optimization method with an iterative grid constraint generation, decoupled from the linear unit commitment problem, is employed. From the considered scenarios, it was found that in a typical German neighborhood with 150 households, PV penetration of ∼5kWp per household can lead to curtailment of ∼60MWh per year due to line loading. Furthermore, the proposed method eliminates grid violations due to the addition of new sectors reducing the curtailment up to 60%. With the optimization of the heat pump operation, an increase of 7% of the self-consumption was achieved with similar results for the combination of battery systems and electrical vehicles. In conclusion, a safer and optimal operation of a complex energy system is fulfilled. Safer control strategies and more accurate plant sizing could be derived from this work.

Learning disorders (LD) are diagnosed in children impaired in the academic skills of reading, writing and/or mathematics. Children with LD usually show a slower resting-state electroencephalogram (EEG), with EEG patterns corresponding to a neurodevelopmental lag. LD-children also show a consistent cognitive impairment in working memory (WM), including an abnormal task-related EEG with an overall slower EEG activity of more delta and theta power, and less gamma activity in posterior sites; task-related EEG patterns considered indices of an inefficient neural resource management. Neurofeedback (NFB) treatments aimed at normalizing the resting-state EEG of LD-children have shown improvements in cognitive-behavioral indices and diminished EEG abnormalities. Given the typical findings of a WM impairment in LD-children; we aimed to explore the effects of a NFB treatment in the WM of children with LD, by analyzing the WM-related EEG power-spectrum. We recruited 18 children with LD (8-10 years old). They performed a Sternberg-type WM-task synchronized with an EEG of 19 leads (10-20 system) twice in pre-post treatment conditions. They went through either 30 sessions of a NFB treatment (NFB-group, n= 10); or through 30 sessions of a placebo-sham treatment (Sham-group, n= 8). We analyzed the before-after treatment group differences for the behavioral performance and the WM-related power-spectrum. The NFB group showed faster response times in the WM-task post-treatment. They also showed an increased gamma power at posterior sites and a decreased beta power. We explain these findings in terms of NFB improving the maintenance of memory representations coupled with a reduction of anxiety.

Three-phase AC electric arc furnace (EAF) is a typical non-linear load, causing many power quality problems. Most of the researches on the voltage problems of EAF mainly focus on the voltage fluctuation, and less on the transient voltage problems caused by EAF short circuit and open circuit. In this paper, the relationship between voltage and current of EAF is obtained by combining hyperbolic function and exponential function, then the white noise and chaotic circuit are added to establish the EAF model which is suitable for the study of voltage fluctuation and transient voltage. This paper analyzes the causes of the transient voltage problem of the EAF, calculates the short-circuit current, reactive impact and the influence on voltage at the point of common coupling (PCC) in the three-phase short-circuit of the EAF, and compares the calculation results with the simulation results to prove the accuracy of this model. The results show that the reactive impact of three-phase short circuit is about twice as much as that of normal operation of EAF, resulting in about 30% voltage sag at the PCC, which is very unfavorable to the power grid. This paper provides reference for transient power quality evaluation and dynamic reactive power compensation of EAF.

This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery-supercapacitor hybrid power system. The hybrid power system consists of a Lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC-DC converter model is derived utilizing the first-principles method and compute the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power ele-ments’ life expectancies and supplies the power system with the required power

The evolution in semiconducting devices has introduced flexible control on a system but it has also penetrated harmonics in the grid, which results in extra electrical stress and load. Limiting the presence of harmonics in the grid and low power factor maintained by consumers has become of utmost priority to the power system analyst. Hence, the need for Active Filter emerges. Here, a real-time simulation, in dSPACE- Targetlink, of shunt harmonics active filter is propounded. The possible basic control strategy used in the simulation is ”Instantaneous Power Theory”. Control logic of two features of Active Filter viz., Global Harmonics Elimination, and Reactive Power Compensation are produced in the paper. The results obtained in ”Software In Loop Mode” of dSPACE are discussed and verified in detail. The richness of the code generated by the dSPACE- Target link can be reasoned by observing the results which are well aligned with the industrial standards regarding the harmonics.

The paper presents the results of energy and environmental analysis of geothermal CHP plant operation in Polish conditions. As the most favorable, for the geothermal conditions prevailing in Poland, the variant of CHP plant based on Organic Rankine Cycle (ORC) has been taken into consideration. As the case study the existing geothermal well located in the town of Konin in the Wielkopolska voivodship has been chosen. The conceptual design of CHP plant has been proposed and evaluated from energy and environmental point of view. As energy performance criteria the non-renewable primary energy consumption has been chosen. In case of environmental performance carbon dioxide emission has been taken as evaluation criteria. The analysis has been performed for different operating conditions and three working fluids.

In this paper, a recent meta-heuristic optimization algorithm called the discrete-continuous hyper-spherical search algorithm is used to solve the mixed-integer nonlinear problem of soft open points (SOPs) and renewable distributed generators allocation along with new network reconfiguration methodology under different loading conditions to minimize the total power loss in balanced distribution systems. Multi-scenario studies, which aim to improve the investigation of the overall performance of the strategies, are conducted on IEEE 33-node and 83-node balanced distribution systems. The contributions of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to the system, are investigated to determine the real benefits gained from their allocation. The results obtained validate, with proper justifications, the effectiveness of allocating both SOPs and renewable distributed generators with the proposed network reconfiguration methodology to provide the best operation of distribution networks with minimum losses and enhanced power quality performance. It was also shown that SOPs successfully assist the growing integration plans of the renewable distributed generators units and can address issues related to voltage violations and network losses efficiently.

This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.

Coal combustion in thermal power plants releases ash. Ash is reported to cause different adverse health hazards in humans and other organisms. Owing to the presence of radionuclides, it is also considered as a potential radiation hazard. In this study, based on the surface radiation measurements and relevant ancillary data, expected radiation risk zones were identified with regard to the human population residing near the Thermal Power Plant. With population density as the risk determining criteria, about 20% of the study area was at ‘High’ risk and another 20% of the study area was at ‘Low’ risk zone. The remaining 60% was under medium risk zone. Based on the findings remedial measures which may be adopted have been suggested.

Abstract Due to the relatively low efficiency of thermoelectric (TE) generators, they have mostly been used in niche applications where the unique properties of TE generators outweigh their lack of efficiency, or conditions where it is more reasonable to base the optimal design on maximum generated power rather than maximum efficiency. The aim of this work is to investigate how the different fundamental transport properties (Seebeck coefficient, thermal conductivity and electrical resistivity) in materials with equal figure of merit (ZT) impact TE module design and maximum power output. Further, we discuss and review the strategies to enhance material properties and the latest studies on the TE module performance. It is shown, that by increasing the power factor by a factor of 15 and decreasing the thermal conductivity by 13.33 in order to maintain ZT=1, power output is increased by 45%. This effect is stronger in lower module fill factor and thermal resistance of the heat source and heat sink.

Generator automatic voltage control device (AVC) is an automatic voltage and reactive power optimization control terminal on the power plant side. Aiming at improving the performance of generator automatic voltage control device, this paper introduces a detection method of AVC substation performance based on virtual dispatching terminal environment. During the testing, the upper computer of redundant power plant is connected to the virtual dispatching terminal environment to simulate the sending instructions of AVC master station, the behavior of AVC sub stations and the excitation system of generators. The performance of AVC substation is evaluated according to the feedback adjustment results, which can effectively detect the abnormal behavior of AVC substation in power plant.

In this paper, a new approach is proposed for designing ultra-low-power FFT (Fast Fourier Transform) system suitable for use in energy harvesting powered sensors. Bit-serial architecture is adopted to reduce the power consumption of butterfly operation. Simulation results show that, compared with state-of-the-art bit-serial and conventional parallel processors, the proposed technique is superior in terms of silicon area, power consumption, dynamic energy use due to variable precision arithmetic. A sample design of a 64-point FFT shows that the implementation can save about 40% area and 36% leakage power compared with a conventional parallel counterpart, accordingly achieving significant power benefits at a low sample rate and low voltage domain. The dynamic variation of the arithmetic precision can be achieved through a simple modification of the controller with hardware area overhead of 10% gate count.

This study analyses the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost (SRMC) curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation capability and the water shadow price. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves.

We study on the sums of powers of consequtive integers and alternating sums of power of consequtive integers. We derive many identities and correlations including Bernoulli, Euler and Genocchi polynomials and numbers.

Supply chain management requires regular updates of the location of assets, which can be enabled by low power wide area networks, such as Sigfox. While it is useful to localize a device simply by its communication signals, this is very difficult to do with Sigfox because of wide area and ultra narrowband nature. On the other hand, installing a satellite localization element on the device greatly increases its power consumption. We investigated using information about nearby Wi-Fi access points as a way to localize the asset over the Sigfox network, so without connecting to those Wi-Fi networks. This paper reports the location error that can be achieved by this type of outdoor localization. By using a combination of two databases, we could localize the device on all 36 test locations with a median location error of 39 m. This shows that the localization accuracy of this method is promising enough to warrant further study, most specifically the minimal power consumption.

The switched inertance hydraulic system (SIHS) is a novel high-bandwidth and energy-efficient digital device which can adjust or control flow and pressure by a means that does not rely on throttling the flow and dissipation of power. An SIHS can provide an efficient step-up or step-down of pressure or flow rate by using a digital control signal. In this article, analytical models of an SIHS in a four-port high-speed switching valve configuration are proposed, and the system dynamics and performance are investigated theoretically and experimentally. The flow responses, system characteristics and power consumption can be predicted effectively and accurately by using the proposed models, which were validated by comparing with experiments and with numerical simulation. The four-port configuration is compared with the three-port configuration, and it is concluded that the former one is less efficient for valves of the same size, but provides a bi-direction control capability. As bi-direction control is a common requirement, this constitutes an important contribution to the development of efficient digital hydraulics.

This article presents a new input device for spastic patients and others with similar symptoms. The sensor consists of a disc used to determine positions and can replace standard joysticks in medical devices such as electric-powered wheelchairs. Using a standard joystick while operating a powered wheelchair can result in dangerous situations when spastic movements occur and extremities cramp making them uncontrollable. To avoid this, a disc was developed that can be controlled with any body part. By shifting weight (x- and y-axis) the disc can tilt in any direction creating a proportional output signal and can also be pushed down in the center (z-axis) to open a menu on a screen for example. When spasms occur it is impossible for users to get stuck on the input device because the disc is flat and can be mounted within a control panel. Body parts coming into contact with the disc would merely slide across the disc without triggering unintentional actions. The sensor presented here is also adaptive and can be adjusted to fit a user’s strength and range of motion. This proposal aims to develop an input device that enables spastic patients to operate sensitive systems safely.

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

In this paper, a small wind power generator is proposed with a new straight shape stator and rotor. The advantages of the proposed structure are introduced through a comparison between the basic and the proposed models. By comparing the pole slot combination of the proposed generator, the combination with optimal cogging torque characteristics was selected. The electromagnetic characteristics of proposed shape are analyzed for design variables using a finite element analysis (FEA) of ANSYS Maxwell. The final model of the proposed structure is designed by considering cogging torque and the generator electromagnetic characteristics. The electromagnetic and structural simulations of the final model are performed to satisfy the required performance of generator and mechanical safety. To verify the FEA results of the final model, a prototype is manufactured, experimented, and compared with the FEA results.

An important issue concerning the inspection of the technical condition of electrical power components and systems is thermal imaging investigation. This paper presents how the thermograms obtained from these measurements should be interpreted correctly according different standards and how operators should react when a specific anomaly is detected. It is also a review article in which all currently applicable international standards are referred to. The motivation of the article relates to the fact that these standards seems to be too general and not cover all practical situations even though in many countries thermal imaging diagnostics of overhead lines or overhead outdoor and indoor power stations has been used for years on the basis of industry standards or good practices. The purpose of writing this article is precisely to encourage and provoke the global community of metrologists, scientists and engineers involved in thermographic measurements to discuss, strengthen efforts and establish relevant international standards for the interpretation of thermograms containing the relevant temperature anomalies.

Electrification of society, increasing renewable energy sources and mobility charging lead to new loading patterns for power transformers. Dynamic load conditions induce enhanced mechanical stress on the transformers’ windings, potentially causing degradation of the solid insulation over time and compromising the transformer's short-circuit withstand capability. Thermal expansion of the windings, caused by losses in the copper conductors, occurring as the transformer is load-ed, increases the stress. Conversely, magnetic losses in the core and tie plate expansion contribute to a reduction in stress. This paper presents the effect of step changes in core losses and copper losses by on-line measurements of the clamping pressure, to better understand the mechanical stresses acting upon the solid insulation cellulose materials.

The traditional approach of considering the probability distribution of rain attenuation leads to overdesign the power margin of data channels. We have first recalled a method, proposed in 2016, which with a small power margin, bandwidth expansion and variable symbol rate avoids overdesign and can transfer the same data volume as if the link were in clear–sky conditions. It is charaterized only by the link mean efficiency 0≤η≤1, suitably defined. It is usefull only if: (a) data must be downloaded also when it is raining; (b) real–time communication is not strictly required. We have applied it to the links of GeoSurf satellite constellations by simulating rain attenuation time series at 80 GHz (mm–wave) with the Synthetic Storm Technique, from rain-rate time series recorded on–site, at sites located in different climatic regions. The power margin to be implemented ranges from 2.0 dB to 7.4 dB – well within the current technology – regardless the instantaneous rain attenuation. The clear–sky bandwidth is expanded 1.75 to 2.80 times, not large per se, but it may challenge current technology if the clear–sky bandwidth is already large.

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen enriched compressed natu-ral gas (HCNG). It is also injected into the natural gas pipeline network to realize the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load peak and trough period is adjusted, and large-scale consumption of renewa-ble energy and the reduction of carbon emissions are realized. First of all, a fine model of hydro-gen production and hydrogen use equipment is established to analyze the impact of adding hy-drogen mixing on the economy and low-carbon of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utiliza-tion rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment opera-tion cost, carbon emission cost, wind curtailment cost and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natu-ral gas was established. The model was linearized and solved by using MATLAB and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

Wireless Sensor Networks are critical for modern applications ranging from environmental monitoring to smart city infrastructures. This research proposes a novel optimization learning and deployment strategy for Wireless Sensor Networks (WSNs) by 3D WUSN model is uniformly distributed M sensor nodes across diverse terrains, with clusters representing 20% of sensors, showcasing its ability to balance power consumption and extend network longevity, grounded in fuzzy clustering based protocol using the Fuzzy C-Means (FCM) method, called FCM-3DWUSN. Comparative analysis against established models including LEACH-C, K-Means and LEACH emphasized FCM-3DWUSN's outperformed performance, with average power consumption such as 134.990, 341.790, 143.895, and 192.984; respectively. The experimental result shows that FCM protocol in the 3D WUSN model can reduce energy consumption and improve the network lifetime to gather data and send the information.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

Soiling accumulated on a photovoltaic (PV) module can significantly reduce the transmittance of the cover glass, resulting in power losses and consequent economic losses. Natural atmospheric parameters influence the accumulation of soiling at the various geographic locations. In this paper, the approaches and outcomes of the research studies on either indoor (simulator-based) or outdoor (field-based) PV soiling have been thoroughly reviewed. Different parameters depicted for the power loss of PV modules are analyzed individually and presented. Moreover, this study delves into a detailed examination of the key factors influencing dust depositions on PV modules in various geographical regions, with a particular focus on their relationship with climatic conditions. This way, probable future research directions to quantify soiling losses are identified. In addition, different loss prevention and mitigation techniques are also reviewed. This makes it possible to highlight effective strategies and pinpoint potential future research lines in these areas.

Due to the intermittency and fluctuation of photovoltaic (PV) output power, a high proportion of grid-connected PV power generation systems has a significant impact on power systems. Accurate PV power forecasting can alleviate the uncertainty of the PV power and is of great significance for the stable operation and scheduling of the power systems. Therefore, in this study, a feature rise-dimensional (FRD) two-layer ensemble learning (TLEL) model for short-term PV power deterministic forecasting and probability forecasting is proposed. First， based on the eXtreme Gradient Boosting (XGBoost), Random Forest (RF), CatBoost, and Long-short-term memory (LSTM) models, a TLEL model is constructed utilizing the ensemble learning algorithm. Meanwhile, the FRD method is introduced to construct the FRD-XGBoost-LSTM (R-XGBL), FRD-RF-LSTM(R-RFL), and FRD- CatBoost - LSTM (R-CatBL) models. Subsequently, the above models are combined to construct the FRD-TLEL model for deterministic forecasting, and perform probability interval forecasting based on quantile regression(QR). Finally, the performance of the proposed model is demonstrated with a real-world dataset. By comparing with other models, the proposed model displays better forecasting accuracy for deterministic forecasting and reliable forecasting intervals for probability forecasting, and good generalization ability in the datasets of different seasons and weather types.

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

A Savonius wind turbine is one of the simplest vertical-axis turbines. Several researchers have been attracted to it because of its simple structure, low noise, appropriate torque, and independence from wind direction. SR3345 and SR5050 blade profiles will be investigated for their effects on the performance of Savonius wind turbines. First, the fabricated wind turbine is placed in front of an open circuit wind tunnel with a velocity of 6 m/s. Furthermore, governing relationships are used to calculate power and torque coefficients as well as tip speed. In this study, the power and torque coefficients of the turbine are compared with the two profiles considered based on overlap ratios between 0 and 0.3. Despite its increased stability, the central shaft reduced the power coefficient of the turbine. For blade SR3345, the best performance occurs at tip speed ratios of less than 1, while for blade SR5050, the best performance occurs at higher tip speed ratios. Both blades SR3345 and SR5050 achieve the highest power coefficients at overlap ratios of 0 and 0.18 when a central shaft is present.

In a previous paper, we solved the partial differential equation of Mullins problem in the case of the evaporation-condensation in electronic devices and gave an exact solution relative to the geometric profile of the grain boundary grooving when materials are submitted to thermal and mechanical solicitation and fatigue effect. In this new research, new modelling of the grain groove profile was proposed and new analytical expressions of the groove profile, the derivative and the groove depth were obtained in the case of diffusion in thin polycrystalline films by the resolution of the fourth differential equation formulated by Mullins that supposed 〖y'〗^2≪1. The obtained analytical solution gave more accurate information on the geometric characteristics of the groove that were necessary to study the depth and the width of the groove. These new findings will open a new way to study with more accuracy the problem of the evaporation-condensation combined to the diffusion phenomenon on the material surfaces with the help of the analytical solutions.

This study examined if people who reside in different regions of South Korea exhibit different WTP for the mitigation of PM2.5 emissions from coal power plants by restricting the operation of such plants during the winter and spring when air pollution becomes severe. A contingent valuation method combined with a double bounded dichotomous choice was used to derive people’s WTP for the mitigation of PM2.5 emissions. Sample group was divided into central, western, eastern, and southern regions. It is known that the eastern region is affected by northwesterly winds in the winter and spring seasons, so air pollutants in the eastern areas fly to the eastern sea. According to the estimation results, respondents who live in eastern regions showed significantly lower WTP than the average WTP. We suggest that the mitigation policy on PM2.5 emissions should consider regional differences in the contributions of coal power plants to PM2.5 emissions.

Top-down models are defined by hardware architects to provide information on the utilization of the different hardware components. The target is to isolate the users from the complexity of the hardware architecture while giving them insight into how efficiently the code is using the resources. In this paper, we explore the applicability of 4 top-down models defined for different hardware architectures powering state-of-the-art HPC clusters (Intel Skylake, Fujitsu A64FX, IBM Power9, and Huawei Kunpeng 920) and propose a model for AMD Zen 2. We study a parallel CFD code used for scientific production to compare these 5 Top-Down models. We evaluate the level of insight achieved, the clarity of the information, the ease of use, and the conclusions that each one allows us to reach.

The variation of mutual inductance and load parameters will affect the transmission power and efficiency of the inductive power transfer (IPT) system. The identification of mutual inductance and load parameters is an essential part of establishing a stable and reliable IPT system. This paper presents a joint identification method of load and mutual inductance for LCC-S IPT system, which does not require the establishment of primary and secondary communication and related control. Firstly, the resistance-inductance characteristics of the equivalent load of the rectifier are analyzed by simulation, and then the rectifier and system load are equivalent to the circuit model of resistance and inductance in series. Secondly, the characteristics of the reflected impedance are analyzed, and the functional relationship between the transmitter impedance and the rectifier impedance is established by using the ratio of the real part to the imaginary part of the reflected impedance, which realizes the decoupling of the load and the mutual inductance. Thirdly, the functional relationship between the equivalent impedance of the rectifier and the load resistance of the system is obtained by data fitting. Then, the equations of the above two functional relationships are combined. By measuring the voltage of the parallel compensation capacitor at the transmitting side, the current of the transmitting coil and the phase difference between the two, the battery load can be solved first, and then the mutual inductance can be calculated, so that the high-precision identification of the load and mutual inductance can be realized. Finally, an experimental platform of LCC-S IPT system is built for experimental verification. The experimental results show that the maximum identification errors of mutual inductance and load are 5.20 % and 5.53 %, respectively, which proves that the proposed identification method can achieve high precision identification.

PM-Gati-Shakti Initiative, integration of ministries, including railways, ports, waterways, logistic infrastructure, mass transport, airports, and roads. Aimed at enhancing connectivity and bolstering the competitiveness of Indian businesses, the initiative focuses on six pivotal pillars known as "Connectivity for Productivity": comprehensiveness, prioritization, optimization, synchronization, analytical, and dynamic. In this study, we explore the application of these pillars to address the problem of "Maximum Demand Forecasting in Delhi." Electricity forecasting plays a very significant role in the power grid as it is required to maintain a balance between supply and load demand at all times, to provide a quality electricity supply, for Financial planning, generation reserve, and many more. Forecasting helps not only in Production Planning but also in Scheduling like Import / Export which is very often in India and mostly required by the rural areas and North Eastern Regions of India. As Electrical Forecasting includes many factors which cannot be detected by the models out there, We use Classical Forecasting Techniques to extract the seasonal patterns from the daily data of Maximum Demand for the Union Territory Delhi. This research contributes to the power supply industry by helping to reduce the occurrence of disasters such as blackouts, power cuts, and increased tariffs imposed by regulatory commissions. The forecasting techniques can also help in reducing OD and UD of Power for different regions. We use the Data provided by a department from the Ministry of Power and use different forecast models including Seasonal forecasts for daily data.

In order to reduce the risk of grid transient voltage instability brought about by the new energy and system dynamic loads and the increasing proportion of new energy access, this paper proposes a differentiated dynamic reactive power compensation configuration method for suppressing transient voltage dip instability. The method first establishes a dynamic reactive power device configuration index system based on a multi-binary table, and then establishes a differentiated dynamic reactive power compensation device configuration model based on the node instability risk characteristics by combining with the regional division of system transient voltage instability risk. The model takes the investment cost and suppression effect as the optimization objectives, and realizes differentiated dynamic reactive power compensation by flexibly adjusting the optimization weights of economic benefits and reactive power compensation effects in the configuration process; Compared with the traditional compensation methods, differentiated compensation can significantly improve the economic benefits of reactive power compensation configuration. Finally, taking the IEEE39 node test system as an example, the improved TLBO algorithm is used to solve the model, which verifies the accuracy and effectiveness of the method proposed in this paper.

This research paper presents a comprehensive study on the optimal planning and design of hybrid renewable energy systems for microgrid (MG) applications at Oakland University. The HOMER Pro platform analyzes the technical economic and environmental aspects of integrating renewable energy technologies. The research also focuses on the importance of addressing unmet load in the MG system design to ensure the university's electricity demand is always met. By optimizing the integration of various renewable energy technologies, such as solar photovoltaic (PV), energy storage system (ESS), combined heat and power (CHP), and wind turbine energy (WT), the study aims to fulfill the energy requirements while reducing reliance on traditional grid sources and achieving significant reductions in greenhouse gas emissions. The proposed MG configurations are designed to be scalable and flexible, accommodating future expansions, load demands changes, and technological advancements without costly modifications or disruptions. By conducting a comprehensive analysis of technical, economic, environmental factors, and addressing unmet load, this research contributes to the advancement of renewable energy integration within MG systems. It offers a complete guide for Oakland University and other institutions to effectively plan, design and implement hybrid renewable energy solutions, fostering a greener and more resilient campus environment. The findings demonstrate the potential for cost-effective and sustainable energy solutions, providing valuable guidance for Oakland University in its search of energy resilience and environmental surveillance which has a total peak load of 9.958MW. The HOMER simulation results indicate that utilizing all renewable resources, the estimated net present cost (NPC) is a minimum of 30M$, with a levelized energy cost (LCOE) of 0.00274$/kWh. In addition, the minimum desired load will be unmeted on some days of September.

In this paper, we compile the fractional power series method and the Laplace transform to design a new algorithm for solving the fractional Volterra integro-differential equation. For that, we assume the Laplace power series (LPS) solution in terms of power q=1m,m∈Z+, where the fractional derivative of order α=qγ for which γ∈Z+. This assumption will help us to write the integral, the kernel, and the nonhomogeneous terms as a LPS with the same power. The recurrence relations for finding the series coefficients can be constructed using this form. To demonstrate the algorithm's accuracy, the residual error is defined and calculated for several values of the fractional derivative. Two strongly nonlinear examples are discussed to provide the efficiency of the algorithm. The algorithm gains powerful results for this kind of problem. Under Caputo meaning the obtained results are illustrated numerically, and graphically. Geometrically, the behavior of the solution declares that the changing of the fractional derivative parameter values' in their domain alters the style of the attained solution in a symmetrical meaning and fully coinciding to the ordinary derivative value'. From these simulations, the results report that the recommended novel algorithm is a straightforward, accurate, and superb tool to generate analytic-approximate solutions for Integral, and integro-differental equations of fractional order.

A block-diagonal fuzzy neural network for short-term load forecasting is proposed. DBD-FELF consists of fuzzy rules with consequent parts that are neural networks with internal recurrence. These networks have a hidden layer which consists of pairs of neurons with feedback connections between them. The overall fuzzy model partitions the input space in partially overlapping fuzzy regions, where the recurrent neural networks of the respective rules operate. The partition of the input space and determination of the fuzzy rule base is performed by use of Fuzzy C-Means clustering algorithm and the RENNCOM constrained optimization method is applied for consequent parameter tuning. The electric load time-series of the Greek power system is examined, and hourly-based forecasting for the whole year is performed. The performance of DBD-FELF is tested via extensive experimental analysis and the results are promising, since an average percentage error of 1.18% is attained, along with an average yearly absolute error of 76.2 MW. Moreover, DBD-FELF is compared with Deep Learning, fuzzy and neurofuzzy rivals, such that its particular attributes are highlighted.

In this contribution, we make an attempt to write a theoretical proposal for designing an eco friendly thermal power plant which runs with cold nuclear fusion technology at a temperature of (1500 to 2000) deg.C. In our recently published papers, we have proposed a clear cut mechanism for understanding and implementing cold nuclear fusion technique pertaining to fusion of hydrogen with metals of mass numbers starting from 50. In this context, we would like to stress the point that, fusion of hydrogen under controllable temperature and pressure can be understood as a phenomenon of fusing neutron to the nucleus of the base atom. Part of isotopic nuclear binding energy difference of final and base atomic nuclides can be seen in the form of safe thermal energy of the order of (1 to 3) MeV per atom against 200 MeV released in nuclear fission of one Uranium atom. Due to increased heaviness and weak interaction, sometimes fused neutron splits into proton and electron. Proton seems to be retained by the base atom’s nuclear core and electron seems to join with the electronic orbits of the base atom. In this way, increased mass of base atomic nuclide helps in eco friendly production of thermal energy in large quantity. For this purpose we consider Iron-56 as a fuel. In a simplified view, under strong nuclear attractive forces, Iron-56 absorbs hydrogen atom as a neutron and by emitting 1MeV equivalent thermal energy transforms to Iron-57. Thus, one gram of Iron-56 can generate 1000MJ of heat with 50% efficiency. In a shortcut approach, by bombarding powder and semi-liquid forms of Iron-56 with direct neutrons coming from neutron source, our proposal can be tried, understood and verified experimentally.

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.