Search Results (1,570)

With increasing renewable and power-electronic penetration, emergency control must convert early post-fault measurements into feasible actions within a short latency budget. This paper proposes a response-driven online framework that uses the simplified branch transient transmission capacity (sBTTC) as a physically interpretable interface between causal stability forecasting, action-response prediction, and constrained control. A preceding masked Informer forecasts the no-control all-branch sBTTC trajectories from the first 1.00 s of measured response; a ConvLSTM then predicts generator-tripping recovery and the recovery associated with four load-shedding levels. These predictions are embedded in a weighted mixed-integer piecewise-linear model with stability-recovery, action-bound, and power-balance constraints. On the studied 100-bus renewable-rich AC/DC system, ConvLSTM achieved an RMSE of $7.193\times {10}^{-3}$ and an MAE of $5.674\times {10}^{-3}$ with 69.16 ms inference time. Its inference was 66.42% faster than Informer, while its RMSE was only 2.06% higher; relative to conventional LSTM, its RMSE and MAE were reduced by 28.21% and 21.53%, respectively. Across 62 grouped out-of-sample disturbances, the validation results give a 96.77% control success rate. In the representative disturbance, 900 MW of generation tripping and 740 MW of load shedding restored the nonlinear terminal sBTTC to 0.998, and the command was issued 1.36 s after fault inception. The framework provides an auditable forecast–response–decision chain; its additive approximation is restricted to the validated action range and uses an empirical 0.05 sBTTC recovery margin selected to exceed the observed 95th-percentile absolute error; this margin is not interpreted as a worst-case error bound. Full article

Battery energy storage systems can reduce the operating cost of active distribution networks while supporting voltage control through their power electronic converters. This paper develops an application-specific multi-operator Differential Evolution (DE) framework for the coordinated active and reactive power scheduling of distributed battery energy storage systems in radial distribution networks with photovoltaic generation. The optimization model minimizes the daily operating cost associated with conventional energy supply, photovoltaic and storage operation and maintenance, and battery degradation. Candidate schedules encode hourly active and reactive power references for three storage converters, producing a 144 dimensional decision vector for a 24 h horizon. Each candidate is repaired to satisfy active power, state of charge, terminal energy, and converter apparent power limits before being evaluated through an alternating current power flow based on matrix successive approximations. The search framework generates three competing trial schedules per target individual by combining established best-guided, random, and current-to-random DE mutation families with a discrete parameter pool, a common feasibility-repair operator, and greedy selection after AC network evaluation. The method is tested on modified 33-node and 69-node active distribution networks and compared with AJAYA, genetic algorithm, multiverse optimizer, and particle swarm optimization. In the deterministic 33-node case, Differential Evolution obtains the lowest best cost, USD 6846.206, and the largest best cost reduction, 2.1838 percent. The scenario study performs separate deterministic optimizations for pre-generated operating realizations and is therefore interpreted as a scenario-conditioned sensitivity assessment rather than as stochastic or robust optimization of one here-and-now schedule. In this assessment, DE achieves the largest average savings: 2.3487 percent in the 33-node network and 2.9314 percent in the 69-node network. Voltage magnitudes, branch loading, converter ratings, and cyclic state of charge constraints are satisfied in all evaluated cases. The results identify the proposed framework as a competitive day-ahead solver within the evaluated cases, while no claim of global optimality or universal superiority over alternative optimizers is made. Full article

AI is becoming a key enabler of high-efficiency and sustainable energy conversion in power converters, electrical machines, electric drives, renewable-energy interfaces, and advanced energy-management systems. This critical review examines how machine learning, deep learning, reinforcement learning, physics-informed models, digital twins, and optimization algorithms enhance the design, control, monitoring, and operation of modern electromechanical energy-conversion systems. The paper outlines the main AI methods relevant to power electronics and electrical machines, distinguishing between data-driven, model-assisted, and hybrid approaches. It summarizes AI applications in power converter design, modulation, fault diagnosis, thermal management, wide-bandgap semiconductor operation, grid-connected renewable energy converters, and electric vehicle thermal and energy management, range characterization, and charging. A dedicated section covers electrical machines and drives, including AI-assisted electromagnetic and thermal design, condition monitoring, sensorless control, efficiency-map optimization, and predictive maintenance. To distinguish the scale of the claimed engineering outcome from the maturity of the supporting evidence, the review introduces an E1–E4 engineering outcome scale together with the AI Energy-Conversion Evidence Maturity (AI-ECEM) M1–M5 scale, a minimum reporting bundle, a net-benefit accounting framework, and a deployment roadmap. The synthesis indicates that surrogate-assisted design and diagnostic classification are comparatively mature, whereas autonomous real-time control and lifecycle deployment require stronger hardware, robustness, cybersecurity, and field evidence. Full article

Microbial fuel cells (MFCs) are promising bioelectrochemical systems for converting organic matter into electrical energy, but their practical relevance depends on both functional performance and sustainability-oriented viability. This study evaluated the bioelectrochemical behavior of double-chamber MFCs inoculated with kefir, comparing graphene and graphite anodes under fed-batch operation. A total of 33 MFC series were monitored longitudinally through voltage, current, power output, substrate consumption, and oxidation-reduction potential. Under the LED-connected closed-circuit configuration used here, kefir-inoculated reactors exhibited a reproducible electrical response together with near-complete substrate depletion. These findings support kefir as a workable mixed inoculum for comparative reactor operation under the tested conditions, although direct extracellular electron transfer and exclusive microbial causation of the measured signal were not demonstrated. Graphene showed higher early and mean electrical performance than graphite, particularly in power-related metrics, although this advantage decreased over time and did not result in a categorical separation of final batch-level outcomes. In contrast, substrate consumption remained highly similar between anode materials, indicating that the main material effect was expressed in electrochemical translation rather than in overall substrate conversion. Taxonomic profiling supported the presence of a metabolically complementary consortium dominated by lactic acid bacteria, acetic acid bacteria, Gram-negative bacteria, and yeasts. Deterministic sensitivity analysis and Monte Carlo-based LCA/TEA screening further showed that the most sustainable scenario was not necessarily the one with the highest electrical response, highlighting the importance of integrating performance, material burden, and uncertainty in MFC assessment. Full article

Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) are emerging as the technology of choice for next-generation power conversion systems, offering switching speeds, on-state resistance, and power density unattainable with silicon or even silicon carbide (SiC) devices. However, the fast switching transients that make GaN attractive also make it a demanding source of electromagnetic interference (EMI), so credible electromagnetic compatibility (EMC) analysis requires an accurate functional device model. This paper addresses the functional electrothermal modeling of a commercial 650 V GaN HEMT (GS66504B) as a prerequisite to EMC validation. The manufacturer-supplied Level 3 SPICE model is evaluated against experimental static (I-V) and dynamic (C-V) measurements. Significant discrepancies motivate an optimization methodology in which an initial manual procedure is superseded by a fully automated pipeline coupling LTspice with a Genetic Algorithm in MATLAB R2025b. A forward/reverse and dual-temperature-segment strategy reduces the mean absolute relative error to below 7% (forward I-V) and 13% (reverse I-V) over 25–100 °C, while a dedicated two-stage C–V optimization reduces the reverse-transfer capacitance error from 95.4% to 2.89%. The resulting compact, unified, and fully validated model underpins the ongoing EMC validation phase, where it will be combined with extracted parasitic and cable models in a DC-DC converter topology. Full article

The proliferation of power converters has posed significant challenges to the simulation of power grids. The unit switching circuit (USC) method provides an approach for power electronics grid simulation, but neglects the situation where the switching action time deviates from the time-step boundary. This article presents novel simulation and solving approaches for converters based on computational equivalence to precisely simulate the power electronics grid. This article also introduces a straightforward calculation method for determining equivalent circuit parameters through port quantity observation to achieve computational equivalence. The qualitative error analysis is given and accompanied by comprehensive discussions on its validity, numerical characteristics, and applicable scenarios. A case study is performed to verify the proposed method’s effectiveness and efficiency, yielding results that demonstrate improved accuracy. Full article

Chaos, characterized by a broad spectrum, aperiodic, unpredictable behavior, and sensitivity to initial conditions, has been widely studied as a potential candidate for secure data transmission. Switching voltage converters (SVCs) are well known for their ability to exhibit nonlinear and, more specifically, chaotic behavior. In contrast to conventional approaches that seek to eliminate chaotic behavior in switching voltage converters, this work proposes exploiting such behavior to generate chaotic oscillations for further use in authentication and physical-layer security systems. However, reliable data recovery in a converter-based chaotic communication system requires synchronization between the transmitter and receiver converters operating in the chaotic regime. This work demonstrates the leader–follower synchronization of chaotic buck converters via control-signal injection using both SPICE simulations and laboratory experiments, contributing to the experimental investigation of chaotic power electronics. Simulation and experimental results confirm synchronization of chaotic buck converters using the proposed method, achieving a high correlation (>0.8) between the output waveforms. Furthermore, the analysis of the effect of noise in the synchronization channel demonstrates that converters remain highly correlated for SNR values down to 20 dB, suggesting their potential applicability to chaos-based communication systems. The proposed method achieves synchronization at the expense of the follower converter’s output-voltage regulation capability and requires both converters to share a common clock source, motivating future research on integrated synchronization and control strategies. Full article

This work provides a systematic literature review on reinforcement learning (RL) for integrated maximum power point tracking (MPPT) and battery management in photovoltaic (PV) systems. As PV installations increasingly incorporate battery energy storage, the traditional objective of maximizing instantaneous power extraction is no longer sufficient on its own, since control decisions also affect battery state of charge, efficiency, degradation, and load support. Although RL has shown strong potential for sequential decision making in energy systems, most existing studies still treat MPPT and battery management as separate or only loosely coordinated problems. This review examines this issue by systematically examining how RL, particularly continuous-action methods, has been applied to coupled PV–battery control. The analysis highlights the shortcomings of discrete-action formulations in power-electronic systems and emphasizes the advantages and limitations of actor–critic approaches such as DDPG, TD3, PPO, and SAC for directly optimizing continuous-control variables. Across the reviewed literature, RL is found to be used predominantly at the supervisory energy management level, with PV generation often treated as exogenous rather than as an explicit control decision. This review therefore identifies a persistent structural separation between converter-level PV control and storage-aware energy management within a common learning and evaluation framework. It identifies continuous-action RL as a candidate formulation for unified PV–battery optimization while highlighting important challenges in constraint handling, state representation, sample efficiency, stability, and hardware validation. Full article

With the integration of renewable energy and power-electronic devices, grid-forming modular multilevel converters (GFM-MMCs) play a critical role in active grid support. An AC grid voltage sag can trigger a large support current, which may cause large voltage fluctuations in submodule capacitors and arm overmodulation, thereby threatening system safety. This paper proposes a multidimensional collaborative method to improve the support capability of grid-forming MMCs under severe grid voltage sags. The multidimensional physical constraints of internal energy fluctuation during fault transients are clarified. The corresponding safe operating boundaries are then established, after which a coordinated optimization strategy is developed. This approach integrates second-harmonic circulating current and zero-sequence voltage injections. Offline optimization utilizes a particle swarm optimization (PSO) algorithm across the full operating range. Expanding the safe P–Q operating region requires no extra hardware costs. A neural network enables a millisecond-level direct mapping control architecture. This architecture addresses the long online computation time of traditional heuristic algorithms by embedding offline optimization data into the network weights. The trained network performs rapid forward computation to generate optimized commands, which is verified by a hardware-in-the-loop (HIL) experiment. The experimental results verify the effectiveness of the proposed method, with clear performance improvements being observed. The strategy suppresses capacitor-voltage peak and prevents overmodulation. This directly improves the MMC support capability during severe faults. Full article

Electric vehicle charging stations (EVCSs) are increasingly deployed as grid-interactive energy assets that combine power electronic converters, sensing devices, communication interfaces, photovoltaic (PV) generation, battery energy storage systems (BESS), and multiple charging ports. This complexity creates reliability challenges because abnormal behavior may originate from electrical, thermal, sensing, communication, port-level, or grid-side sources. This paper proposes a fault-aware decision-support framework for renewable-powered EVCSs using multi-source explainable learning. The framework integrates electrical, thermal, session/port, grid/PV/BESS, and communication/data-quality indicators into a unified health-monitoring representation. Supervised models diagnose known fault classes, anomaly-detection models flag unknown or anomalous events, and a source-level explainability layer supports candidate-source interpretation and maintenance-oriented risk mapping. A scenario-controlled EVCS benchmark is developed with PV generation, BESS operation, grid import, charging-port behavior, communication/data-quality indicators, and six injected fault/anomaly categories. An extended 180-day benchmark further assesses longer-horizon operation, seasonal/weather diversity, drift/ageing proxies, and event-level behavior. The strongest closed-set classifier, LightGBM with class weights, achieved 98.45% accuracy and 0.9792 macro-F1, while the Random Forest model used for explainability and decision-layer analysis achieved 97.35% accuracy and 0.9626 macro-F1. Full multi-source monitoring improved Random Forest macro-F1 from 0.6922 under electrical-only monitoring to 0.9626, demonstrating within the controlled benchmark the diagnostic value of heterogeneous EVCS observability. Open-set performance was source dependent: sensor/measurement and communication/data anomalies were more detectable, whereas thermal/cooling and port/session unknowns remained difficult at the selected threshold. Under nominal scenario-based response assumptions, unavailable port hours and unmet charging energy decreased by 49.01% and 38.33%, respectively, relative to reactive operation; sensitivity analysis showed that these outcomes depend on intervention effectiveness and response delay. These findings establish controlled-benchmark feasibility for explainable multi-source EVCS decision support. Field validation using charger telemetry, maintenance-confirmed labels, and operator-calibrated response policies remains necessary. Full article

Passive single-phase to three-phase conversion represents an attractive alternative for low-power applications, particularly in isolated systems and rural electrification scenarios where simplicity, robustness, and low cost are essential. However, these passive topologies inherently produce voltage unbalance whose magnitude strongly depends on load characteristics. This work analyzes a passive single-phase to three-phase converter based on reactive elements and a transformer, focusing on the limits of voltage unbalance mitigation through discrete transformer tap optimization. The study is conducted under steady-state sinusoidal conditions using phasor modeling and symmetrical component analysis. The voltage unbalance factor (VUF) is adopted as the primary optimization metric, while the current unbalance factor (IUF), neutral current, and converter losses are used as complementary performance indicators. Results indicate that transformer tap optimization can reduce voltage unbalance for specific load conditions, although low residual unbalance is achieved only near the nominal operating point. Higher residual unbalance is observed as the load becomes more inductive within the investigated power-factor range. The findings indicate that passive single-phase to three-phase conversion can be technically viable for low-power applications with relatively stable load conditions. However, applications requiring high power quality or dynamic regulation may benefit from active converter solutions based on power electronics. Full article

This paper addresses the need for cost-effective and integrated impedance spectroscopy (IS) techniques for supercapacitors (SCs), particularly in applications where conventional frequency response analyzers (FRAs) are impractical due to their high cost and lack of portability. A power converter-based methodology is proposed to perform IS using a power electronics interface, enabling in situ characterization of SCs. The approach is based on an analytical formulation that relates the amplitude of the duty-cycle perturbation introduced into the converter with the excitation frequency and the desired sinusoidal current amplitude, allowing the direct generation of frequency-dependent excitation signals using the power converter. The proposed methodology is first validated using circuital simulations, demonstrating accurate impedance estimation with a Range-Average Absolute Error (RAAE) of $0.30\%$ in magnitude and $1.94\%$ in phase compared with a reference simulation. Experimental validation is then conducted using a synchronous converter controlled by a digital signal processor, and those results are benchmarked against a commercial FRA, obtaining an experimental RAAE of $4.88\%$ in magnitude and $2.41\%$ in phase. These discrepancies are mainly attributed to limitations in the excitation and measurement stages. In addition to its accuracy, the proposed approach significantly reduces implementation cost. The converter-based setup relies on standard power electronics hardware and conventional laboratory instrumentation, with an estimated cost of approximately $\$2000$ USD, which is much cheaper than commercial FRA-based systems (up to $60,000 USD). These results demonstrate that the proposed methodology provides a practical and scalable alternative for impedance spectroscopy of supercapacitors, enabling embedded and in situ diagnostics of energy storage systems. Full article

Autonomous robotic systems designed for extraterrestrial in situ resource utilization (ISRU) will play a central role in enabling a sustained human presence on the Moon and Mars. These robots are expected to perform tasks such as regolith excavation, water extraction, oxygen production, and propellant generation under extremely harsh environmental conditions, including large temperature variations, abrasive dust, high radiation levels, and significant communication delays with Earth. Consequently, their onboard electrical systems must operate with high reliability, autonomy, and fault tolerance. A critical enabling technology for these systems is the isolated power conversion architecture, which distributes energy from primary power sources to multiple robotic subsystems, including mobility actuators, drilling systems, sensors, computing units, and thermal management modules. Future lunar and Martian missions are expected to rely on a combination of alternative energy sources, including solar photovoltaic arrays with energy storage, fuel cells, radioisotope power systems, and nuclear surface power reactors, which can provide continuous and high-density energy independent of sunlight availability. These diverse power sources require flexible and highly efficient isolated DC–DC power conversion architectures capable of managing wide input voltage ranges while ensuring electrical isolation, safety, and system stability across distributed robotic platforms. This literature review surveys recent developments in autonomous isolated power conversion architectures suitable for lunar and Martian resource extraction robots. The review examines advanced converter topologies such as resonant converters, phase-shifted full-bridge converters, dual-active bridge converters, and modular multiport power converters designed for high efficiency, high power density, and scalable power distribution. Emphasis is placed on converter architectures capable of interfacing with nuclear-powered systems and other high-energy-density sources while supporting distributed loads in robotic mining and processing systems. In addition, the paper reviews emerging autonomous control strategies, including adaptive digital control, intelligent power management, fault detection and self-recovery mechanisms, and distributed power architectures capable of maintaining stable operation under dynamic load conditions. The role of wide-bandgap semiconductor technologies, including silicon carbide (SiC) and gallium nitride (GaN), is also examined, highlighting their potential to enable higher switching frequencies, improved efficiency, reduced system mass, and enhanced thermal performance in vacuum environments. Finally, system-level considerations for integrating isolated power conversion within robotic ISRU platforms are discussed, including redundancy strategies, power bus architectures, electromagnetic compatibility, thermal management, and long-duration reliability requirements. By consolidating advances across power electronics, autonomous control, and space power systems, this review identifies key research gaps and outlines design directions for next-generation autonomous power conversion systems capable of supporting scalable lunar and Martian resource extraction infrastructures powered by both renewable and nuclear energy sources. Full article

The ambitious roadmap for a sustainable transport system adopted by the European Commission (EC) by 2050 includes the deployment of an extensive Electric Vehicle Charging Stations (EVCSs) infrastructure, which introduces significant challenges for distribution power grids. High power demand, particularly from fast-charging systems, may lead to network overloading and voltage unbalance. In addition, recent measurement campaigns highlight substantial changes in grid impedance and the emergence of resonance phenomena, together with the injection and propagation of high-frequency conducted disturbances. These effects extend over a wide frequency range, up to several hundreds of kHz, causing degradation, aging and malfunction of network assets, in particular Power Line Communications. This paper provides a comprehensive and updated review of the impact of EVCSs on electrical grids, covering power flow, power quality, stability, and impedance-related interactions. Particular attention is given to the role of power-electronic converters, high-frequency emissions, and the associated challenges in measurement and standardization. The analysis highlights that EVCS integration fundamentally alters the nature of electrical loads, requiring new approaches for grid planning, monitoring, and regulation. The study identifies key research gaps and outlines future directions to ensure the reliable and sustainable integration of electromobility into modern power systems. Full article

The growing accumulation of coal combustion by-products necessitates the development of sustainable approaches for their utilization in construction materials. This study investigates the production of sintered fly ash aggregate (SFAA) using hydraulic ash waste from the Kyzylorda Combined Heat and Power plant and evaluates its suitability as a coarse aggregate for lightweight cementitious composite. Hydraulic fly ash and clay from the Talsuat deposit were pelletized and sintered at 1100 °C. The physicochemical, mineralogical, and microstructural characteristics of the raw materials and produced aggregate were examined using X-ray fluorescence, X-ray diffraction, scanning electron microscopy with energy-dispersive spectroscopy, Fourier-transform infrared spectroscopy, and thermogravimetric analysis. The developed aggregate exhibited a bulk density of 1118 kg m⁻³, water absorption of 5.4%, crushing strength corresponding to grade M200, and frost resistance of at least F35. Mineralogical analysis revealed quartz and mullite as the predominant crystalline phases, while microstructural observations confirmed the formation of a stable porous aluminosilicate matrix. Chemical durability tests in alkaline, chloride, and sulfate media demonstrated high resistance to aggressive environments. Lightweight cementitious composite produced with the aggregate achieved an average density of 1657 kg m⁻³, compressive strength of 3.87 MPa, and water absorption of 16.0%, corresponding to density grade D1600 and strength class B3.5. The results confirm the feasibility of converting hydraulic ash waste into a durable lightweight aggregate suitable for structural-insulating lightweight cementitious composite, contributing to waste valorization, conservation of natural resources, and sustainable construction practices. Full article