Abstract:

To address the long-term operational challenges of space environment monitoring buoys under extreme Arctic conditions, this paper proposes an energy management optimization method based on deep reinforcement learning algorithms. By constructing a buoy system model integrating renewable energy and lithium-ion battery power supply units, battery energy storage units, and multi-sensor load units, and incorporating Arctic environmental models with low-temperature battery efficiency degradation patterns, a reward function was designed to minimize unsupplied energy while ensuring functional integrity. Using the Twin Delay Deep Deterministic Policy Gradient (TD3) algorithm on the MATLAB simulation platform, the effectiveness of different energy storage configurations for achieving long-term observation in Arctic environments was compared. Results demonstrate that this method significantly enhances the buoy’s endurance and scheduling intelligence, offering new insights for energy management in intelligent polar observation equipment.

Abstract: This study analyzes the frequency response of a common-emitter bipolar junction transistor (BJT) amplifier using Fourier methods. We developed a small-signal model and applied Fourier analysis in MATLAB/Simulink to characterize the amplifier's gain and bandwidth. Key performance metrics, including mid-band gain and lower/upper cut-off frequencies, were extracted. The analysis is based on the concept of a transistor with a copper-enhanced base region, which is known to reduce base resistance and improve high-frequency performance. This work demonstrates the practical application of the Fourier transform for converting time-domain circuit behavior into a frequency-domain transfer function, providing a clear methodology for high-frequency amplifier analysis.

Abstract: Microgrids, as localized and flexible power systems capable of operating in both grid-connected and islanded modes, have introduced significant challenges in traditional power system analysis due to the high penetration of Distributed Energy Resources (DERs). These challenges particularly relate to short-circuit current (SCC) calculation and relay protection (RP) coordination, where conventional methods often fail to account for bidirectional power flows, inverter-based resources, and dynamic topologies. This paper presents a review of existing approaches to short-circuit analysis and relay protection coordination in microgrids. Through a critical examination of recent literature and practical implementations, we identify the current gaps and limitations in prevailing methodologies. Based on this review, the most promising methods for short-circuit calculation and relay protection coordination are selected and subjected to an in-depth analysis. These methods are applied and evaluated on a real-life microgrid system using ETAP (Electrical Transient Analyzer Program). Simulation results and performance assessments are presented, highlighting the strengths and weaknesses of the selected approaches. The findings provide valuable insights into current limitations and offer concrete directions for future research and development in the domain of microgrid protection and reliability.

Abstract: This study examines the impact of increasing photovoltaic (PV) penetration on the transient stability of the IEEE 9-bus power system. Synchronous machines are modeled with standard subtransient dynamics, while PV units are represented as current-limited grid-following inverters. Transient stability is assessed through the Critical Clearing Time (CCT) and the post-fault dynamic behavior, obtained from time-domain simulations carried out in MATLAB/Simulink®. A permanent three-phase fault on line 7–5 is considered as the limiting contingency. The results show an increase in CCT as PV generation progressively replaces the active power supplied by synchronous machines, whose inertia is therefore maintained: from 210 ms (0% PV) to 440 ms (25%) / 1080 ms (40%) at bus 5, 410 ms (25%) / 1130 ms (40%) and 290 ms (25%) / 650 ms (40%) at buses 6 and 8, respectively, demonstrating that the injection site is a key factor for system stability. For distributed injection among the three buses, CCT values of 340 ms (25%) and 1020 ms (40%) highlight the significant influence of PV placement at bus 8. Although an overall increase in CCT was observed, higher PV penetration also led to more pronounced oscillations and operability issues after the fault. These results underscore the need for stability-oriented control strategies, such as grid-forming operation, fast active power support, and dynamic voltage control. They also suggest that planning practices should favor interconnections electrically closer to the slack generator. Overall, a high PV penetration level—modifying only the operating point of synchronous machines—allows longer fault durations to be tolerated; however, appropriate siting of PV units and the adoption of advanced inverter controls could mitigate the observed oscillations and post-fault operability challenges.

Abstract: This paper proposes an analog retuning strategy that strengthens the functional longevity of photovoltaic (PV) systems operating within circular-economy environments. Although PV modules can be relocated from large generation sites to low-demand rural or remote settings, their electrical behavior offers no adjustable quantities capable of extending service duration. In many cases, even after formal disposal or decommissioning, these solar panels still retain a considerable portion of their energy-generation capability and can operate for many additional years before their output becomes negligible, making second-life deployment both technically viable and economically attractive. In contrast, the associated power-electronic converters contain modifiable gate-driver parameters that can be reconfigured to moderate transient phenomena and lessen device stress. The method introduced here adjusts the external gate resistance in conjunction with coordinated switching-frequency adaptation, reducing overshoot, ringing, and steep dv/dt slopes while preserving the original switching-loss budget. A unified analytical framework connects stress mitigation, ripple evolution, and projected lifetime enhancement, demonstrating that deliberate analog tuning can substantially increase the endurance of aged semiconductor hardware without compromising suitability for second-life PV applications. Experimental results validated the study, confirming the effectiveness of the proposed approach for long-term deployment.

Abstract: Asynchronous motor drives are often better without a rotational speed sensor because removing the encoder or resolver gets rid of one of the weakest and most troublesome elements of the whole drive system, while modern control algorithms can usually estimate speed with sufficient accuracy for most applications. A shaft encoder or resolver is a delicate device mounted on the motor shaft, exposed to heat, vibration, dust, moisture and electrical noise. It needs precise mechanical mounting, alignment, extra cabling and connectors, and it ages or fails much faster than the motor itself. In many industrial and marine installations, unplanned stoppages are caused more by damaged encoders or their cables than by real motor failures. Another issue connected with sensorless operation is a flying start (also called flying restart, spin-catch or catch-on-the-fly) which is a special function of an inverter drive that allows it to safely take control of an induction machine that is already rotating, instead of assuming the motor is at standstill. With flying start mode enabled, the drive first looks for the actual speed and direction of the motor before it builds up torque. In practice, it reconnects to the motor with a special search routine: it applies a small voltage pattern, measures the resulting currents and the back EMF, and from these signals estimates the rotating magnetic field and thus the motor’s speed and direction. Once it has locked onto the rotor flux, it adjusts its output frequency and phase so that the stator field “catches” the spinning rotor smoothly. After that, it ramps the speed to the reference value just like in normal operation. The asynchronous generator needs an upgrade in comparison to drive mode of operation which means that startup conditions differ than this used in the motoring mode. This article presents original method of sensorless rotational speed determination and excitement process with use of machine state observer.

Abstract: This paper presents the design and implementation of a 64-Multi-Phased Time-to-Digital Converter (TDC64) architecture on a low-cost Cyclone V FPGA. Operating at 500 MHz, the architecture successfully achieves a theoretical resolution of 31.25 picoseconds (ps). The modular design leverages a multi-phased counter methodology to significantly enhance temporal granularity. The performance was comprehensively characterized in two stages. Internal analysis, using the Signal Tap Logic Analyzer, confirmed the design's integrity, yielding a measurement result of 9660 for a 300 ns interval, representing a low deviation of approximate 0.62 %. Linearity tests conducted over a 20 ns span showed excellent performance with differential nonlinearity (DNL) ranging from +0.053 to -0.101 and integral nonlinearity (INL) between -0.192 and -0.218. External testing, utilizing a waveform generator and oscilloscope, revealed an uncompensated resolution of 47.43 ps (34 % deviation). Mitigating the noise, the compensated resolution for an interval of 300 ns result in 9,596, a resolution of 31.263 picoseconds, which represents 0.04%. A remarkable result close to the theoretically expected value. The mean values were employed to evaluate the linearity, yielding DNL of +0,0560 and -0,0129, as well as INL of +0,0484 and -0,2104. Representing high linearity and high resolution compared with a previous noisy entry signal. This work demonstrates that high-end timing performance is attainable on cost-effective FPGA platforms.

Abstract: Radar signal coherent integration technology is a critical method to improve the performance of detection systems. However, existing techniques face challenges regarding real-time performance and the flexibility of multi-pulse coherent accumulation. In this paper, a dynamically configurable multi-pulse multi-frame real-time coherent integration system based on FPGA is designed and implemented, and the dynamic configuration of the number of pulses and the number of frames stored for each pulse is realized through the host computer. The experimental results show that the output signal delay of coherent integration is 33 microseconds at 40 pulses, and the energy gain reaches 16 dB at 40 pulses, which provides a dynamically configurable hardware platform and solution for real-time coherent integration of high-frame-count, multi-pulse radar signals.

Abstract: Reliable detection of the onset of accelerated degradation is essential for safe and cost-effective operation of lithium-ion batteries. This paper proposes a Bayesian changepoint model designed for ``smart sensing'' in battery management systems (BMS), where intelligence is applied to standard voltage–current–temperature (V–I–T) telemetry rather than new sensing hardware. We define a simple, interpretable health indicator (HI)--the ratio of charge time to discharge time--computed directly from cycle-level BMS-compatible features. A probabilistic, piecewise-linear model with a single changepoint is fitted using Hamiltonian Monte Carlo, yielding posterior distributions for the onset of accelerated degradation and for pre/post-change slope behaviour. The method provides calibrated uncertainty intervals that can be integrated into risk-aware BMS decision processes, addressing the limitations of deterministic breakpoint heuristics that supply only point estimates. Using an open dataset of 18650 lithium-ion cells, the model consistently identifies a mid-life transition in the HI trajectory and demonstrates good predictive adequacy through posterior predictive checks. The approach is computationally lightweight, transparent, and directly compatible with embedded implementations. By transforming standard BMS telemetry into an uncertainty-aware degradation signal, the proposed framework supports the development of intelligent and deployable sensing strategies for next-generation battery systems.

Abstract: This paper proposes a substation operation risk situational awareness method based on the health state of main equipment, with the objective of assessing the substation operation risk posture and performing risk prevention and control based on the situational awareness model. Firstly, the time-varying failure rate corresponding to the risk propagation moment of the main equipment of the substation is obtained based on the equipment failure rate model considering the effect of equipment deterioration; Secondly, the risk propagation model considering the health state of the main equipment is proposed with reference to the SI virus transmission model to simulate the risk propagation process among the main equipment of the substation; Then, the construction of equipment potential risk severity index to realize the substation operation risk posture presentation; Finally, based on the results of the substation operation risk situation assessment, the main equipment operation and inspection plan is optimized to formulate the main equipment operation and inspection plan, and the defects of the main equipment are found in time to carry out overhauling and maintenance. The example analysis is carried out using a dual-voltage level substation, and the results show that the method proposed in this paper can effectively analyse the substation operation risk situation.

Abstract: The increasing complexity of smart buildings and the tightening requirements of European regulations have amplified the need for integrated modeling environments capable of assessing automation and control strategies in early design stages. This paper presents a digital-twin-based simulation framework that links building information modeling (BIM) data with MATLAB/Simulink models of heating, ventilation, and air conditioning (HVAC), lighting, and shading systems to support the standardized evaluation of Building Automation and Control Systems (BACS) according to EN ISO 52120 and the Smart Readiness Indicator (SRI). The proposed framework enables rapid scenario development, systematic comparison of automation classes, and assessment of predictive control concepts within a unified, regulation-aligned environment. A representative academic building is used to demonstrate how the toolchain supports consistent simulation of operational states, subsystem interactions, and energy-related behaviors across varying control strategies. The results highlight the ability of the framework to capture performance trends, support compliance-driven decision-making, and provide a basis for the gradual introduction of predictive and artificial intelligence (AI)-enabled methods. The approach offers a structured path to more advanced digital-twin-driven building design, verification, and optimization.

Abstract: Since 2014, the Mexican Caribbean has faced an ecological and socio-economic crisis due to massive coastal landings of pelagic sargassum. This study presents a comprehensive methodology for sargassum detection using Machine Learning (ML) models applied to imagery from a coastal video monitoring station (EVMC). Three machine learning techniques were implemented to classify sargassum on sand and water, Support Vector Machines (SVM), Random Forest (RF), and a Multi-Layer Perceptron (MLP) Artificial Neural Network. The performance of these ground-based models was then compared against sargassum detections from Sentinel-2 satellite data using the Floating Algae Index (FAI) and the Normalized Difference Vegetation Index (NDVI). The results demonstrated high efficacy, with the MLP model proving most effective for detecting sargassum on sand with a F1-score > 0.86, whilst the Random Forest model performing best in water with a F1-score > 0.75. A significant positive correlation was found between the video-based detections and indices derived from satellite data, with NDVI showing a consistently stronger correlation in both environments. This study validates coastal video monitoring as a reliable tool for local sargassum quantification and suggests that fusing this high-resolution ground data with wide-area satellite imagery offers a promising path toward a more accurate and comprehensive sargassum monitoring system.

Abstract: This project aims to develop a basic predictive maintenance model for aviation sensors, especially the ones that are directly related to flight safety. The study first uses SCAPS-1D to simulate a simple semiconductor structure and observe how electrical parameters such as Voc and Jsc change with temperature and material conditions. After getting these results, a simple state-space model is built in MATLAB to represent the relation between temperature input and device output. The idea is that many aircraft sensors, like the Angle of Attack (AOA) sensor, also contain small pn junction electronics inside, and their failures often begin with small drifts in electrical behavior. By comparing the SCAPS results with the state-space model, the project shows how these changes can be used as early indicators of degradation. This work gives a starting framework for future predictive maintenance studies by connecting semiconductor modeling with aviation sensor health monitoring.

Abstract: A sustainable city requires a sustainable means of transportation. This ambition is leading towards a higher penetration of electric vehicles (EVs) in our cities, in both the private and commercial sectors, putting more and more burden on the existing power grid. Modern deregulated power grids vary electricity tariffs from location to location and from time to time, to compensate for any additional burden. In this paper, we propose a profit-aware solution to strategically manage the movements of EVs in the city to support the grid while exploiting these locational, time-varying prices. This work is divided into three parts: M1) Profit-aware charging location and optimal route selection, M2) Profit-aware charging & discharging location and optimal route selection, and M2b) Profit-aware charging & discharging location and optimal route selection considering the demand-side flexibility. This work is tested on the MATLAB programming platform using the Gurobi optimisation solver. From the extensive case study, it is found that M1 can yield profits up to 2 times more than those of its competitors, whereas M2 can achieve profits up to 2.5 times higher and simultaneously provide substantial grid support. Additionally, M2b extension has made M2 more efficient in terms of grid support.

Abstract: This paper assesses the steady-state voltage impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, representing approximately 20% and 40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking (∂V/∂Q), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow study is performed using Monte Carlo sampling, which jointly models residential-demand variability and stochastic EV charging activation. Whenever the expected minimum-hourly voltage violates the 0.95 p.u. threshold, a closed-form sensitivity-guided reactive compensation is computed and injected at the critical bus, and the power flow is re-solved. Results show that ultra-fast charging can produce sustained under-voltage even under robust siting, particularly at high penetration and 1 MW ratings; however, the proposed compensation consistently raises the minimum-voltage trajectory by about 0.03–0.12 p.u., substantially reducing the depth and duration of violations. The cross-case comparison confirms that lowering unit charger power mitigates voltage degradation and reactive-support requirements, while charger clustering accelerates stability-margin depletion. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-stability assessment and targeted mitigation in EV-rich distribution networks.

Abstract: The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device level due to limited computing resources, mobility, and the large diversity of devices, as well as at the network level, where the use of varied protocols by different vendors introduces further vulnerabilities. Physical Unclonable Functions (PUFs) provide a lightweight, hardware-based security primitive that exploits inherent device-specific variations to ensure uniqueness, unpredictability, and enhanced protection of data and user privacy. Additionally, modeling attacks against PUF architectures is difficult to execute due to the random and unpredictable physical variations inherent in their design, making it nearly impossible for attackers to accurately replicate their unique responses. This study collected approximately 80,000 Challenge Response Pairs (CRPs) from a Ring Oscillator (RO) PUF design to evaluate its resilience against modeling attacks. The predictive performance of five machine learning algorithms, i.e., Support Vector Machines, Logistic Regression, Artificial Neural Networks with a Multilayer Perceptron, K-Nearest Neighbors, and Gradient Boosting, was analyzed, and the results showed an average accuracy of approximately 60%, demonstrating the strong resistance of the RO PUF to these attacks. The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. The p-values from the 15 statistical tests confirm that the CRP data exhibit true randomness, with most values exceeding the 0.01 threshold and supporting the null hypothesis of randomness.

Abstract: In this paper, the leakage inductances influences of integrated-transformer are investigated for an input-series flyback converter, in which each input-series circuit is based on the single-switch flyback topology. First, configuration of this converter is introduced, and a novel multiple inductors coupling model is proposed for its flyback integrated-transformer. Second, operational process of this converter is analyzed considering the leakage inductances between primary and secondary windings of its integrated-transformer. Third, influences of these leakage inductances are analyzed, on this basis, the essential design considerations of flyback integrated-transformer are summarized. Finally, an experimental prototype of this input-series converter is built, based on which, the analysis is verified by the experimental comparisons among three flyback integrated-transformers with various windings layouts.

Abstract: Accurate and rapid measurement of junction temperature is critical for optimizing the performance and ensuring the longevity of a super luminescent diode. However, due to diverse diode structure, direct measuring and monitoring the junction temperature of a super luminescent diode are often challenging and impractical. We propose a non-invasive methodology to precisely determine the junction temperature and spectral characteristics of a super luminescent diode. This method utilizes a modified static modulated Fourier-transform spectrometer alongside a generalized analyzing expression derived from Gaussian components. Fast acquisition of spectral information is achieved through the modified static modulated Fourier-transform spectrometer and analyzing method. The proposed model exceptional accuracy, yielding an average coefficient of determination R2, of 0.99 across a range of operating currents and junction temperatures. Our analysis reveals a distinct linear correlation between the extracted fitting parameters-specifically, the carrier temperature, the spectral shape parameter and the physical junction temperature. These findings demonstrate that critical internal physical conditions of the diode can be accurately inferred directly from its measured spectrum, providing a robust tool for device characterization.

Abstract:

Forests play a vital role in influencing wind flow by modifying turbulence intensity and vertical wind shear. As wind turbines are susceptible to these conditions, accurately describing wind flow in forested environments is vital for ensuring structural reliability and realistic energy yield assessments. In Latvia, where approximately 51,3% of the territory is covered by forests, the likelihood of wind turbine deployment in such areas is considerable. However, wind behaviour within and above forests is complex and strongly influenced by canopy effects, which in turn affect wake dynamics, structural fatigue, and power production. Advancing research in this field is therefore crucial for improving the accuracy of wind resource assessment and supporting evidence-based engineering solutions that enable the sustainable development of wind energy. Moreover, a better understanding of forest–atmosphere interactions contributes to more precise estimations of the Levelized Cost of Energy (LCOE), as accurate wind flow modelling directly impacts energy yield predictions, project feasibility, and long-term economic performance.

Abstract: Silicon carbide (SiC) power converters are increasingly used in automotive, renewable energy, and industrial applications. While reliability assessments are typically performed at either the device or system level, an integrative approach that simultaneously evaluates both levels remains underexplored. This article presents a novel system-level simulation method with two strategies to evaluate the reliability of power devices and a resonant converter under varying temperatures and total ionizing doses (TIDs). Temperature sensitive electrical parameters (TSEPs), such as on-state resistance (R_ON) and threshold voltage shift (ΔV_TH), are calibrated and analyzed using a B1505A curve tracer. These parameters are incorporated into the system-level simulation of a 300 W resonant converter with a boosting cell. Both Silicon (Si) and SiC-based power resonant converters are assessed for power application in space engineering and harsh environments. Additionally, gate oxide degradation and ΔV_TH-related issues are discussed based on the simulation results. The thermal-strategy results indicate that SiC MOSFETs maintain more stable conduction loss at elevated temperatures, exhibiting higher reliability due to their high thermal conductivity. Conversely, increased TIDs result in a negative shift in conduction losses across all SiC devices under the radiation strategy, affecting the long-term reliability of the power converter.