Search Results (1,208)

To address the issue of significantly reduced coupling coefficient and limited transmission efficiency in traditional flat solenoid magnetic couplers within wireless power transfer (WPT) systems under horizontal lateral offset conditions, this paper proposes a design method for a concave flat solenoid coil magnetic coupler for engineering applications, aiming to achieve high misalignment tolerance. An equivalent model of the LCC/S compensation circuit is established, its output characteristics are analyzed, and the parameter configuration method for its resonant elements is derived. Secondly, from the perspective of winding arrangement, the mechanism by which the coil winding method, turn spacing, and port concavity angle affect the uniformity of magnetic field distribution and the retention rate of the coupling coefficient is analyzed in detail, and corresponding parameter trade-off and optimization methods are proposed. Subsequently, a simulation model of multiple configuration magnetic couplers is established based on Ansys/Maxwell, comparing the magnetic field distribution and coupling coefficient variation of different structures under horizontal offset conditions. The results show that the concave structure with a non-uniform arrangement and a port concavity angle of 30° can still maintain a high coupling coefficient and stable transmission performance under a maximum horizontal offset equal to 60% of the corresponding transmitter-side characteristic dimension. To achieve lightweight and integrated design, the receiver is designed with a flexible printed circuit board (FPC) coil structure, meeting the miniaturization and high power density requirements of low-to-medium power portable devices. Finally, a 100 W experimental prototype was built. Experimental results show that within an offset range of ±15 mm on the X-axis and ±30 mm on the Y-axis at the receiver, the system output voltage fluctuation is controlled within 4%, and the maximum transmission efficiency reaches 87.3%. These results verify the feasibility and practical applicability of the proposed magnetic coupler with high misalignment tolerance. Full article

Steel tape armored multicore cables are critical components in the transmission and distribution of power in medium- and low-voltage networks. It is difficult to measure current in the individual conductors of multicore cables because they are enclosed within multilayer protective structures (e.g., armor). The magnetic field–current inversion method provides a noncontact alternative for measuring conductor currents, derived from externally measured magnetic fields. However, the nonlinear magnetization effects of the steel tape armor disrupt the linear relationship between the magnetic field and currents, making accurate measurements challenging. To address this issue, we propose a noncontact current measurement method that incorporates the nonlinear effects of the armor layer. This method involves pre-calibrating the coefficient matrices, determining the angle formed between the magnetic sensor array and the multicore cable, and applying nonlinear fitting. This achieves a current measurement accuracy less than 5% and 5° in relative error and phase error, respectively. The proposed method avoids computationally intensive inverse operations, thereby enabling the realization of lightweight, low-cost current measurement terminals for practical field applications. Full article

Lightning-induced breaking accidents of medium-voltage insulated conductors pose a serious threat to the safety of distribution networks, and the key cause lies in the establishment and sustained combustion of the power-frequency follow-current arc after lightning overvoltage breakdown. This paper systematically investigates the formation mechanism and critical conditions of power-frequency follow-current arcs using combined simulation and experimental approaches. Based on the streamer discharge theory, a lightning breakdown model was established and combined with the arc energy balance equation, revealing that the establishment of power-frequency follow-current arcs is essentially determined by the post-breakdown energy competition process. The simulation results show that the required anode electric field strength for lightning breakdown is not less than 3 kV/mm. When the power-frequency voltage reaches 10 kV, Joule heating of the arc continuously exceeds heat dissipation loss, enabling restrike after zero-crossing and sustaining stable burning. Experiments verified this voltage threshold and further revealed that the arc establishment rate exhibits nonlinear growth with increasing power-frequency voltage, exceeding 90% at power-frequency voltages ≥ 10 kV. The study also reveals that increased gap distance reduces the arc establishment rate, while the introduction of insulators can enhance it by approximately 20%. This study clarifies the energy criterion for power-frequency follow-current arc establishment and the influence patterns of key parameters, providing theoretical basis and engineering reference for lightning protection design and arc suppression in medium-voltage insulated lines. Full article

Modern microgrids increasingly rely on learning-based energy management systems (EMSs) for real-time decision-making, yet remain vulnerable to cyber–physical disturbances, sensor tampering, and model uncertainty. Existing resilient control and robust reinforcement learning methods provide useful foundations, but rarely address adversarial measurement perturbations that distort belief evolution under partial observability. This gap is critical, as structured perturbations in sensing channels can destabilize learning-based policies and propagate into voltage-regulation violations. This paper proposes an adversarially robust reinforcement learning framework for energy management with voltage regulation under partial observability in microgrids. The EMS decision-making problem is formulated as a partially observable Markov decision process (POMDP) that accounts for adversarial measurement perturbations, belief evolution, and system-level economic and voltage constraints. To avoid excessive conservatism under worst-case uncertainty, an adversary-aware belief construction based on adversarial belief balancing (A3B) is employed to focus on policy-relevant perturbations. Building on this belief representation, an adversarially robust learning framework is developed by incorporating adversarial counterfactual error (ACoE) as a learning regularization mechanism, enabling a balance between nominal operating efficiency and robustness under adversarial measurement distortion. The case study is conducted on a medium-voltage radial distribution feeder (IEEE 123-Node Test Feeder). Case study results demonstrate that the proposed ACoE-regularized policies substantially reduce voltage-deficit events, improve policy stability, and maintain operational constraints under adversarial perturbations, consistently outperforming standard proximal policy optimization (PPO)-based controllers. These results indicate that counterfactual-aware, belief-based learning substantially enhances voltage quality and operational resilience in microgrids with high penetration of distributed energy resources. Full article

Aiming at the problem that insulation fault location in multi-branch cable networks of train DC medium-voltage power supply systems is disturbed by impedance mismatch at the injection end, this paper proposes an impedance matching method based on a resistance–capacitance composite coupling network to suppress false reflections and improve location accuracy. Firstly, the mechanism of multiple false reflections caused by injection-end mismatch is theoretically analyzed, and the expression of reflected waves and their influence on waveform superposition are derived. On this basis, an RC coupling network is designed to achieve effective matching between the measurement end and the cable characteristic impedance (about 97 Ω) within the 8–12 MHz frequency band, suppressing the amplitude of false reflections to less than 2% of the incident wave. Verification through MATLAB R2022b/ANSYS Q3D 2024R2 co-simulation and a 1:8 scaled experimental platform shows that the proposed coupling network reduces the absolute fault location error in a multi-branch network from 6.936 m to 0.188 m, decreases waveform distortion by about 40.8% and lowers the equivalent noise enhancement factor by about 55.2%. This study provides a reliable front-end matching solution for accurate fault location in complex cable networks, with clear value for engineering applications. Full article

This paper reports initial experimental results related to the preparation of colored anodic titania thin layers using various deep eutectic solvent (DES)-based formulations. Electrolytes based on choline dihydrogen citrate–oxalic acid–ethylene glycol (1:1:1 molar ratio), choline chloride–oxalic acid (1:1 molar ratio) and choline chloride–lactic acid (1:2 molar ratio) eutectic mixtures were investigated. The anodization has been performed at constant voltage in a range of 10–100 V for various periods of time between 1 and 5 min at room temperature under mild stirring. A brief description of anodization procedures, as well as of some characteristics, from appearance and morphological viewpoints, is presented. A quantitative analysis of color characteristics in relation to the DES-based electrolyte and applied voltage using the CIELAB system is also discussed. The achieved chromatic scale follows this order of colors: golden—blue—light blue—light blue/green—pink—violet. This depends on the applied potential and the DES-based electrolyte. The films present a relatively high brightness and color saturation. The hue vs. anodization voltage diagrams suggest an almost linear dependence of the oxide growth measured against the applied voltage. The corrosion performance has been assessed through continuous immersion tests in (i) 0.5 M NaCl for 240 h and (ii) Hank’s biological solution for 96 h with intermediate visual examinations and recording corrosion potential, as well as potentiodynamic polarization curves and impedance spectra at open circuit potential. Different corrosion performances are discussed considering the aggressive medium involved and the used DES-based systems. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

The rapid growth of renewable electricity generation introduces technical challenges that were previously uncommon. These include, for example, problems with exceeding the permissible voltage values in network nodes, overloading of transformers and line sections located behind the transformer, as well as balance problems. This article proposes an original methodology for eliminating these problems. Four objective functions reflecting different operator priorities were used. Attention is drawn to the increasing importance of the development of electricity storage. The results confirm that coordinated optimisation of voltage regulation, energy storage, and flexible load management enables increased renewable energy connection capacity while reducing power losses and improving the grid voltage profile. The case study results demonstrate the effectiveness of the proposed approach under the considered operating scenarios. The proposed tool can support network operators in managing MV grid operation under the considered scenarios. The ongoing energy transition requires network operators to react quickly to emerging problems. Therefore, advanced computational methods are needed to mitigate operational risks and respond to emerging constraints. Full article

Increasing variability in electricity load patterns, driven by end-use behaviour, grid-related technological changes, and socio-economic factors, calls for more accurate and efficient short-term load prediction (STLP) models. This study evaluates the predictive performance of four hybrid models for short-term Amp-load prediction: Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO), as well as convolutional neural networks (CNN) integrated with long short-term memory (LSTM) and extreme gradient boosting (XGB). The models were developed using hourly Amp-load data collected from a power utility substation in Kenya, together with corresponding meteorological data (temperature, wind speed, and humidity) covering a period from January 2023 to June 2024. Results show that the ANFIS-PSO and ANFIS-GA models outperform the CNN-based models, achieving MAPE values of 4.519 and 4.363, RMSE values of 0.3901 and 0.4024, and R² scores of 0.8513 and 0.8481, respectively, due to the adaptive nature of ANFIS, which enables effective modelling of the irregular, nonlinear, and complex temporal behaviour of the Amp load. Enhanced prediction accuracy was observed across all models when variational mode decomposition (VMD) was applied to pre-process the input data. This result was corroborated through further analysis of the Amp-load signals using Taylor plots. Among all of the configurations tested, the CNN-LSTM-VMD model exhibited the highest overall prediction accuracy, with MAPE of 2.625, RMSE of 0.1898, and R² of 0.9702, marginally outperforming the ANFIS-PSO-VMD model, thus making it more suitable for short-term load prediction applications. Full article

The use of photovoltaic (PV) water pumping technology offers a viable and sustainable alternative to conventional diesel-driven pumping systems. In PV-based pumping installations, the elimination of bulky transformers significantly reduces the overall system size and weight, which is particularly advantageous for rural and remote irrigation applications. However, removing the transformer can result in high common-mode voltage (CMV) when the induction motor is controlled using a direct torque control (DTC) scheme. This elevated CMV induces leakage currents that may damage the motor, compromise system reliability, and pose potential safety hazards. To ensure a more compact and safer PV pumping system, this paper introduces an improved DTC-based control strategy for induction motors driven by transformerless multilevel PV inverters. The proposed approach effectively suppresses leakage current by mitigating its main source, CMV, while maintaining the simple structure and dynamic performance inherent to conventional DTC. Two new look-up tables (LUTs) are developed to control the stator flux and electromagnetic torque while simultaneously eliminating leakage current. The first method, termed zero-medium vector DTC (ZMV-DTC), employs both zero and medium voltage vectors from the space vector diagram. The second, referred to as medium vector DTC (MV-DTC), utilizes only medium vectors. Numerical simulation results validate the feasibility and superior performance of the proposed algorithms in terms of leakage current suppression. Compared with a conventional DTC (C-DTC) scheme that is designed to limit the CMV, the proposed DTC algorithms achieve a much stronger reduction in the CMV, confining its amplitude to only a few volts, instead of the levels ±V_dc/6 typically produced by the C-DTC. As a result, the leakage current is effectively eliminated, ensuring safer and more reliable operation of the system. Full article

Rapid electrification of road transport and growing shares of variable renewable generation are pushing urban low-voltage feeders toward their operating limits. Uncoordinated electric vehicle (EV) charging can create transformer overloads, voltage violations, and unfair delays, while most existing smart charging schemes either ignore distribution network constraints or treat fairness and risk in an ad hoc way. This paper proposes a city-scale hierarchical scheduling framework that coordinates EV charging and vehicle-to-grid (V2G) services under renewable variability. In the upper layer, a LinDistFlow-based optimal power flow computes feeder-constrained power envelopes and shadow prices over a rolling horizon, capturing transformer and voltage limits under photovoltaic (PV) uncertainty. In the lower layer, each station solves a queue-aware receding-horizon optimization that allocates charging/V2G set points across plugs using α-fair and lexicographic objectives, with conditional value-at-risk (CVaR) constraints on waiting times and state-of-charge (SoC) shortfalls. A digital twin of a medium-sized city with 24 stations (238 plugs) on five feeders and PV shares between 25% and 55% is used for evaluation. Compared with uncoordinated charging and myopic baselines, the proposed scheduler reduces feeder peak loading and PV curtailment while improving user experience and equity: average waits and 90% CVaR of waits are lowered, the Gini coefficient of waiting times drops (e.g., from 0.31 to 0.22), and SoC shortfalls are significantly reduced, all while respecting voltage limits. Each receding-horizon step executes in under 30 s on commodity hardware, indicating that the framework is practical for real-time deployment in city-scale smart charging platforms. Full article

Efficient internalization of chemical entities into cells across the plasma membrane barrier is crucial for most biomedical applications. Among the physical factors that can enhance the delivery of active substances, the electric field plays an important role. In our experiments, electron paramagnetic resonance (EPR) spectroscopy was used to monitor the process of electric field-assisted endocytosis of a spin probe TEMPO (2,2,6,6-tetramethylpiperidine 1-oxyl) into yeast cells (Saccharomyces cerevisiae). It was found that a long train of low-amplitude voltage pulses (0.5–20 V_pp) can affect the uptake of nitroxide by cells. In addition to the pulse amplitude, the key parameters influencing the internalization process are: frequency, duty cycle and time of exposure to the electric field. The sodium chloride content in the medium in which the cells are suspended is also of great importance. For higher salt concentrations, starting from 3.5% w/w, a significant decrease in cell survival was observed after exposure to an electrical stimulus, which also translated into a decreasing spin probe uptake. Our studies confirm the usefulness of EPR spectroscopy in the study of internalization processes and show that the parameters of electric field pulses and the environmental salinity have a significant impact on the course of electroendocytosis. Full article

This review analyzes the transition from silicon to wide-bandgap (WBG) and ultrawide-bandgap (UWBG) semiconductor materials for power electronics, focusing on Silicon Carbide (SiC) and Gallium Nitride (GaN) technologies. Following a PRISMA-based systematic review methodology, we analyzed 94 peer-reviewed publications spanning device technology, converter architectures, and system applications. We employ a bottom-up approach, progressing from fundamental material properties through device architectures and converter topologies to system-level implications. We examine how intrinsic material properties enable operation at elevated temperatures, voltages, and frequencies while minimizing losses. Through analysis of Figures of Merit and system-level Key Performance Indicators, we quantify WBG benefits across automotive, industrial, renewable energy, and consumer electronics sectors, demonstrating 3–5× power density improvements and 20–40% cost reductions. The review presents emerging device technologies, including vertical GaN for medium-voltage applications and monolithic bidirectional switches (BDSs), enabling single-stage power conversion. We provide the first comprehensive topology-level comparison of emerging vertical GaN and monolithic bidirectional switches against established SiC solutions, identifying specific applications where each technology offers advantages. A comprehensive topology-by-topology comparison between SiC and GaN is provided, offering design guidelines for device selection. The review addresses practical constraints, including dynamic on-resistance degradation, threshold voltage instability, and electromagnetic interference challenges for both SiC and GaN. Finally, we examine emerging UWBG materials ($\beta$-Ga₂O₃, AlN, c-BN, Diamond) and their development status, manufacturing challenges, supply chain considerations, and commercialization prospects for ultra-high-voltage applications. Full article

The continued growth of electric vehicle (EV) deployment has placed increasing emphasis on the development of charging infrastructure that is efficient, reliable, and compliant with safety requirements over a wide range of power levels. In EV charging systems, DC–DC converters work as a key interface for voltage adaptation, power regulation, and battery protection, making the choice of converter topology a crucial design consideration. This study provides a comparative and application-focused review of commonly employed isolated and non-isolated DC–DC converter topologies used in EV charging architectures. The comparison is carried out by examining voltage gain behavior, efficiency tendencies, switching and thermal stress, soft-switching capability, component utilization, control complexity, cost-related aspects, and practical deployment constraints. Fundamental operating principles and representative time-domain simulations are used to highlight relative performance trends of PWM-based and resonant isolated converters under typical charging conditions. Rather than introducing new converter structures or control methods, the objective of this work is to offer practical, design-oriented insights that support informed topology selection. Based on the comparative analysis, non-isolated converters are found to be well suited for low- to medium-power onboard charging applications, whereas isolated resonant converters are more appropriate for high-power and fast-charging systems when safety, scalability, efficiency trends, and system-level implementation factors are considered together. Full article

The integration of renewable energy sources, including photovoltaic (PV) and fuel cell (FC) systems, into AC grids has attracted immense research interest in recent times. Furthermore, incorporating these renewable sources of energy into medium-voltage grids is garnering increased attention because of the obvious benefits of medium-voltage integration at elevated power levels. Photovoltaic applications entail the arrangement of solar panels capable of outputting voltages up to 1.5 kV; nonetheless, fuel cells display restricted output voltage, with a maximum market range of 400 to 700 V. Hence, the efficient integration of renewable energy sources into low-voltage or medium-voltage grids demands the utilization of a step-up direct current (DC–DC) inverter and a converter for connection to the alternating current (AC) grid, in which an efficient step-up converter is critical for the medium-voltage grid. Therefore, this study presents a three-phase buck-boost split-source inverter (BSSI) that resolves the constrained output voltage of the fuel cells. This study focuses on modifying the configuration of a conventional three-phase split-source inverter (SSI) circuit by adding a few components while maintaining the inverter’s modulation. This novel circuit design enables the reduction in voltage strains on the inverter switch components and improves DC-link use in relation to a traditional SSI configuration. For an 800 bus, maximal voltage stress on the primary inverter switches is lowered when compared with the standard SSI that delivers entire DC-bus voltage to switches. A rectifier-based model is employed to simulate the behavior of a renewable energy source. Combining these advantages with the conventional modulation of the inverter offers a more effective design. The buck-boost split-source inverter (BSSI) was analyzed using three distinct modulation techniques: the sinusoidal pulse-width modulation scheme (SPWM), the third-harmonic injected pulse-width modulation (THPWM) scheme, and space vector modulation (SVM). The proposed analysis was validated through MATLAB-SIMULINK and practical outcomes on a 5.0 kW model. The practical and SIMULINK data were found to be closely aligned with the analysis. The circuit developed in this study also ensures efficient DC-to-AC conversion, specifically with regard to low-voltage sources, like fuel cells or photovoltaic (PV) systems. Full article