Search Results (713)

Energy storage systems (ESSs) play a crucial role in mitigating the intermittency and variability of renewable energy sources (RESs) and enhancing the stability and reliability of modern power systems. However, the inherent limitations of individual storage technologies, particularly the trade-off between energy density and power density, restrict their ability to satisfy diverse operational requirements. In this context, hybrid energy storage systems (HESSs), which combine complementary storage technologies, such as batteries, supercapacitors, and flywheels, have emerged as an effective solution capable of simultaneously delivering high-energy and high-power performance. This paper presents a comprehensive review of HESS architectures, converter topologies, energy management strategies (EMSs), and applications. The EMS taxonomy is organized into classical and intelligent control. Classical EMS approaches are categorized into filtration-based, rule-based, deadbeat, droop, sliding mode, and fuzzy logic control, whereas intelligent EMS approaches encompass optimization-based methods, including model predictive control, as well as learning-based techniques such as supervised and reinforcement learning. Moreover, HESS applications are examined across grid-scale systems, microgrids, renewable energy systems, transportation, power quality improvement, frequency regulation, peak shaving, and uninterruptible power supply systems. Representative implementations are also reviewed to identify current technological trends, operational challenges, and performance trade-offs. Finally, future research directions are outlined, with emphasis on digital twins, privacy-preserving and explainable learning frameworks, cyber–physical security, and adaptive and scalable EMSs. Full article

Field voltage testing after the maintenance of large converter transformers requires high-voltage response verification while preserving the restored equipment boundary. However, conventional direct voltage application on the grid side usually requires the disconnection of high-voltage leads and auxiliary devices, which may alter the original electrical boundary and increase field disturbance. To address this issue, this study develops an integrated framework with three core contributions: a minimal-lead-disconnection induced-voltage testing topology, a boundary-specific engineering-equivalent parasitic-capacitance model, and a terminal-referred phasor-based power-supply capacity-calibration method. Under the proposed testing topology, single-phase power-frequency excitation is applied on the valve side, and the induced-voltage response is established at the grid-side bushings while some of the restored auxiliary-equipment connections are retained. Considering the parasitic capacitance introduced by valve towers, tubular busbars, grading fittings, and grid-side auxiliary devices under the minimal-lead-disconnection boundary, an engineering equivalent model for extracting the valve-side stray capacitance is developed based on quasi-static electric field theory, geometric-envelope dimensional reduction, and conformal-mapping-based edge correction. The grid-side equivalent capacitance is further obtained using equipment parameters. On this basis, the induced-voltage distribution under the interconnection of multiple converter transformers is analyzed, and a power-supply capacity-calibration method considering the phasor relationship between inductive excitation current and capacitive current is established. Pre-test calculations yield a valve-side stray capacitance of 0.96 nF and a grid-side equivalent capacitance of 1.27 nF. When the grid-side induced voltage of phase C reaches 9.90 kV, the induced voltages of phases A and B are 4.56 kV and 5.14 kV, respectively, while the apparent power calculated from the field-measured RMS voltage and current is 1.34 kVA. The results verify the effectiveness of the proposed method for low-disturbance field testing and portable test-power-supply configuration. Full article

Power IoT acts as the final frontier of the modern grid, where the reliability of energy supply hinges on precise monitoring. However, current systems often suffer from delayed responses, poor feature separation, and a computational wall when dealing with high-frequency data on the edge. We move past the traditional reactive detection mindset and propose STF-MKD, a framework built on spatio-temporal-frequency graph decoupling and Mamba-WKAN knowledge distillation. Our goal is to shift the operational focus from responding to failures to forecasting them. The first part of the system is the STF-Extractor. It uses dynamic graph attention to map the connections between nodes and a masking game to pull structural features out of the background noise. Following this, we address the wild nonlinear nature of equipment failure with the Mamba-WKAN backbone. By embedding Mexican Hat wavelets and B-splines into the Mamba architecture, the model maintains efficiency while splitting the work: splines track the daily cycles and wavelets lock onto sudden transients. To prevent the model from smoothing away rare anomaly signals, we introduce the TGAR (Teacher-Guided Anomaly-focused Reconstruction) distillation scheme. This one-teacher-two-students setup uses a teacher model with a global view to guide the student predictor. In doing so, the system triggers early warnings based on faint structural shifts before a fault fully develops. Tests on six major datasets, including ETTh/m and WADI, show that STF-MKD outperforms mainstream methods. Full article

This paper presents the experimental validation of a high-frequency full-SiC railway auxiliary converter power module intended as a building block for modular multi-system railway auxiliary converters. The proposed architecture employs a unified SiC-based power conversion platform that integrates an active front-end single-phase PWM rectifier with a galvanically isolated high-frequency DC/DC stage operating at 90 kHz under zero-current switching (ZCS) conditions. Although the converter is designed for both AC and DC traction systems, this study focuses primarily on its operation under single-phase AC railway supply conditions, which are representative of practical applications. A hybrid bipolar–unipolar modulation strategy is used to reduce the RMS voltage stress on the input inductor while preserving controllability of the input current near the voltage zero-crossing regions. Special attention is given to operation under distorted railway supply voltages, which are common in real traction systems. The control structure combines a proportional–resonant (PR) current controller, harmonic compensators, feedforward voltage compensation, and MSOGI-based synchronization to ensure stable synchronization and low-input current distortion even under non-ideal conditions. Experimental validation was performed on a 10 kW laboratory prototype. The results demonstrate a peak efficiency of 98.4% and near-unity input power factor. Under heavily distorted supply conditions ${\mathrm{THD}}_{\mathrm{v}}>30\%$, the input current distortion remained below ${\mathrm{THD}}_{\mathrm{i}}=2.3\%$. Harmonic and STFT analyses confirmed the robustness of the proposed synchronization and current control structure. The obtained results indicate that the proposed high-frequency full-SiC converter topology is a promising solution for future modular railway auxiliary converters, offering high efficiency, reduced passive component volume, and high power density. Full-scale high-voltage validation under both AC and DC traction systems remains the subject of further work. Full article

The rapid growth of artificial intelligence (AI) data centers introduces highly variable and mission-critical load profiles that challenge conventional power supply strategies. This paper proposes an islanded microgrid gas turbine generator (GTG) and long-duration energy storage (LDES) hybrid architecture to provide both short-term load balancing and extended energy support under prolonged outage conditions. A probabilistic multi-phase workload model is developed to capture the temporal characteristics of training, fine-tuning, and inference processes, incorporating both high-frequency fluctuations and multi-day workload variations. Based on reliability requirements, an LDES sizing methodology is formulated to ensure long-duration autonomy for mission-critical operation in a 12 MW power-block AI data center system, with the storage capacity determined based on a 12-h autonomy criterion. The GTG operating point is then evaluated using four storage performance metrics: charge/discharge transition frequency, charging time ratio, state-of-charge (SoC) deviation, and cumulative energy movement. The results indicate that the optimal GTG operating point ranges from approximately 40–73.3% of the initially selected rating, closely tracking the time-varying average load and significantly reducing LDES utilization and storage stress. While GTG fixed-output operation may induce SoC drift under sustained workload variations, applying the identified optimal operating point maintains SoC within the desired range, demonstrating stable LDES operation without dynamic adjustment. The proposed framework provides quantitative design and operational guidelines for GTG–LDES hybrid systems in next-generation AI data centers. Full article

This paper presents a low-power fully integrated sine signal generator for on-chip bioimpedance spectroscopy applications. The circuit is based on a relaxation oscillator, which generates a triangular signal, followed by a sixth-order Gm-C bandpass filter (BPF) that linearizes the waveform. Both blocks, designed in a 0.18 $\mathrm{\mu }$m CMOS process with 1.8 V supply, make use of a current division technique to generate low-frequency signals without requiring high-valued passive components. The relaxation oscillator features an extended frequency tuning range from 300 Hz to 300 kHz, controlled via a tuning current and a digital capacitor bank. The sine output waveform spans from 1 kHz to 50 kHz, and exhibits a −48.8 dB total harmonic distortion at 10 kHz with 18 mV amplitude. The overall system area is 0.7 ${\mathrm{mm}}^2$ and the power consumption is lower than 30 $\mathrm{\mu }$W. Full article

The increasing penetration of inverter-based renewable energy sources and the growing share of underground cable lines significantly modify the frequency-dependent characteristics of medium-voltage distribution networks, increasing the risk of harmonic resonance. Existing resonance studies are often based on detailed electromagnetic models that are difficult to apply during routine distribution network planning and operation. This paper proposes an engineering-oriented methodology for the preliminary assessment of harmonic resonance risk using an equivalent lumped-parameter model that incorporates overhead and cable lines, transformer inductance, photovoltaic generation, and the short-circuit strength of the supplying system. The methodology is applied to a representative 20 kV distribution network to investigate the influence of cable penetration, photovoltaic capacity, transformer loading, and grid strength on resonance conditions. The results show that increasing network capacitance and reducing short-circuit power shift the resonance frequency toward lower-order harmonics, increasing the probability of harmonic amplification. The highest resonance risk is observed under the combined conditions of high photovoltaic generation, low transformer loading, and weak-grid conditions. Unlike detailed electromagnetic simulation models, the proposed methodology enables rapid engineering assessment using parameters readily available to distribution system operators, thereby supporting network planning and operational decision-making in medium-voltage distribution systems with high photovoltaic penetration. Full article

The complex and nonlinear load characteristics of Hall thrusters remain a key challenge in the design of propulsion power-supply output stages. In existing power-supply simulations for electric propulsion systems, the Hall thruster is often simplified as a fixed impedance or a prescribed current source, which makes it difficult to capture the time-synchronized interaction during simulation between the power-supply output stage and the thruster discharge process. To address this issue, this study encapsulates a one-dimensional discharge model as an externally callable thruster slave and proposes a HallThruster.jl–Simulink–Saber co-simulation method. The proposed method enables synchronized bidirectional exchange between the power-port voltage $V_{cmd}$ and the thruster discharge current $I_{out}$. The results show that the discharge current under the co-simulation condition exhibits a sustained low-frequency response at approximately 15.0 kHz. Compared with a fixed-voltage standalone simulation, the co-simulation preserves the same principal oscillation band and overall internal-field structures, while small but observable differences remain in instantaneous phase, local waveform shape, harmonic amplitudes, and high-gradient regions of the internal fields. The proposed method provides a computational framework for investigating dynamically coupled port behavior between a Hall thruster and a representative power-supply output stage. Full article

Traditional approaches for monitoring aquatic organic carbon, such as satellite remote sensing and automated underwater sensors, are often constrained by limited temporal resolution, data gaps under cloudy conditions, maintenance requirements, and cost-effectiveness. To overcome these limitations, we developed and field-demonstrated a ground-based hyperspectral remote sensing system (GHRSS) for continuous, high-frequency monitoring of dissolved organic carbon (DOC) and particulate organic carbon (POC). The system is based on the above-water method and integrates three miniature hyperspectral spectrometers to measure water-surface radiance, sky radiance, and downwelling irradiance for deriving hyperspectral remote sensing reflectance ($R_{rs}$). The spectrometers cover 400–900 nm with a spectral resolution of 1 nm and support a minimum sampling interval of 10 s. The GHRSS also integrates solar power supply, 4G communication, and a microcomputer, enabling autonomous long-term deployment and wireless data transmission. Based on the GHRSS, retrieval models for DOC and POC were developed and validated using 90 paired in situ measurements collected from the Cao’e River. Empirical and machine learning methods were applied to retrieve DOC and POC from the measured $R_{rs}$ data. The empirical models showed limited retrieval performance, whereas partial least squares regression (PLSR) and support vector regression (SVR) substantially improved model accuracy. Among all models, SVR achieved the best performance on the independent test set, with $R^2=0.979$, RMSE = 0.031 mg/L, and MAE = 0.024 mg/L for DOC and $R^2=0.960$, RMSE = 0.152 mg/L, and MAE = 0.066 mg/L for POC. Using the optimal SVR models, minute-scale time series of DOC and POC were reconstructed from the GHRSS observations. The results revealed pronounced sub-daily variability in both parameters, with DOC varying relatively smoothly, whereas POC exhibited stronger short-term fluctuations and more rapid responses to hydrodynamic changes. These findings demonstrate that the GHRSS, combined with machine learning models, provides an effective and practical approach for continuous, high-frequency monitoring of riverine organic carbon dynamics. Full article

Increasing global insecurity for potable water has led to atmospheric water harvesting as a viable supplementary source. Passive dew water harvesting is simple to carry out but atmospheric conditions determine the frequency and amount of dew that can be collected, and up to 0.5 L/m²/night can be considered as an upper ceiling. Thus, active condensers using refrigeration and cooling systems have been developed to increase collection totals, requiring an electrical or solar power supply. In the last decade, adsorption/absorption techniques of water vapor have been studied to maximize collection, with the potential for low costs, portability, and high volumes, and they are operational even in arid regions with low humidity, islands, and remote regions. This could become a gamechanger in securing affordable potable water. Citizen Science is suggested for dew observation and collection data to increase observation points that could be used to improve the resolution/accuracy of local, regional, or global dew modelling. It would promote environmental and water literacy by engaging participants ranging from primary school communities to senior individuals. Teleconferencing now provides access to a worldwide audience and the inclusion of participants no matter their location. Full article

Accurate ocean wave sensing in polar and other low-temperature marine environments is of great significance for marine environmental observation, climate research, and navigation safety. However, conventional wave sensors rely on external power supplies and suffer from poor stability under low-temperature and high-salinity conditions, making long-term self-powered waves sensing a significant challenge. Herein, a highly stable composite eutectogel electrode is developed by integrating sodium lignosulfonate, Fe³⁺ crosslinking, Zn²⁺-carboxylate coordination interactions, and a choline chloride/urea deep eutectic solvent (DES). The DES effectively suppresses solvent crystallization and endows the gel with excellent low-temperature tolerance, while the synergistic effect of metal coordination and multiple non-covalent interactions constructs a robust ion-conducting network with enhanced structural stability. Furthermore, eutectogel-based composite electrode architecture is designed to improve electrical conductivity and charge collection efficiency, thereby enabling stable electrical output under harsh marine conditions. Based on the as-prepared eutectogel electrode, a self-powered solid–liquid triboelectric nanogenerator is fabricated for ocean wave-motion sensing. The device can detect the wave amplitude, with an accuracy of 0.2 cm, and sense the frequency of waves ranging from 0.2 Hz to 1.6 Hz. More importantly, the SL-TENG exhibits excellent environmental adaptability, operating reliably in 3.5 wt% simulated seawater and at 0 °C. The current retention ratio reaches approximately 91% at 0 °C, which is significantly higher than that of the hydrogel-based device (≈6%). The remarkably low-temperature and salt-tolerant performance originates from the stable ion-transport network and anti-freezing characteristics of the eutectogel electrode. This work provides an effective strategy for constructing environmentally resilient eutectogel-based triboelectric devices and offers a promising route toward self-powered wave sensing systems for long-term deployment in harsh marine environments. Full article

This paper presents a digitally controlled current-mode synaptic weighting cell in 28 nm FD-SOI CMOS for low-complexity Multi-Layer Perceptron (MLP) inference in event-based Non-Intrusive Load Monitoring (NILM). A compact bias-free [16,16] MLP is trained offline by backpropagation for fixed-weight feedforward inference. Using 616 ON/OFF events extracted from high-frequency REDD measurements, six appliance classes are characterized by four event-level features: active-power variation, reactive-power variation, current total harmonic distortion of the differential event signature, and event interval. With 16-level input quantization, the model achieves 92.9–93.4% test accuracy and 90.5–90.7% test Macro-F1, requiring 320 weighted-sum branches across two hidden layers. A four-input first-hidden-layer weighted-sum unit is selected as a representative circuit instance. Its computation is mapped to bounded current ranges using current-coded inputs, an 8-bit magnitude-controlled current-mode multiplier, sign-bit current steering, differential current accumulation, and signed-current scaling. The circuit contribution focuses on the weighted-sum datapath, particularly the repeated synaptic weighting cell; activation and complete classifier implementation are outside the scope of this work. Transistor-level PVT and supply-variation simulations validate the signed weighted-sum path, while post-layout extraction evaluates the repeated multiplier cell. The results demonstrate the block-level feasibility of digitally programmable current-mode synaptic weighting for compact event-based NILM inference. Full article

This study proposes an intelligent speed-loop controller for a brushless DC motor (BLDCM) drive implemented under a field-oriented control (FOC) scheme. The controller is constructed by embedding the coati optimization algorithm (COA) into a composite sliding mode theory (CSMT) control structure. In sliding mode control (SMC), the use of only one reaching law normally produces a design trade off: increasing the reaching speed tends to aggravate overshoot or chattering, whereas reducing switching activity often slows convergence. To mitigate this compromise, the proposed controller adopts a composite reaching law (CRL) formed by an exponential component and a power component. When the system trajectory is distant from the sliding surface, the exponential component strengthens the reaching action and shortens the transient interval. When the trajectory moves close to the sliding surface, the power component decreases the effective switching intensity, thereby attenuating high-frequency chattering and reducing the overshoot associated with an aggressive exponential action. For adaptive gain selection, the COA search variables are chosen as four controller parameters: the sliding mode gain, the exponential reaching gain, the power reaching gain, and the power exponent. The fitness index is established from the rotor-speed tracking error and the time variation in that error. By imitating the hunting and predator-avoidance behaviors of coatis, the optimization process updates candidate solutions and selects the parameter combination that best matches the current operating condition. The resulting gains are supplied to the composite sliding mode controller (CSMC) so that the BLDCM can follow speed commands rapidly while preserving stable regulation. Because the proposed method performs online optimization of controller gains rather than data-driven training, it can be realized without a large training dataset. MATLAB/Simulink simulations are carried out to examine the effectiveness of the proposed strategy. The controller is compared with four benchmark methods, namely power reaching law (PRL)-based SMC, exponential reaching law (ERL)-based SMC, non-optimized composite reaching law SMC, and zebra optimization algorithm (ZOA)-assisted ERL-based SMC. The simulation results demonstrate that the proposed COA-based composite sliding mode controller improves both speed command-tracking and load-disturbance rejection relative to the comparative controllers. Full article

High-efficiency and high-power-density point-of-load (POL) converters are critical for data center power supplies. Although hybrid resonant switched-capacitor (ReSC) converters can substantially reduce the volume of passive components, they often suffer from severe efficiency degradation when the switching frequency mismatches the resonant frequency due to component tolerances. To address this challenge, this paper proposes a parameter-mismatch insensitive cascaded POL converter by integrating a BUCK stage with a cascaded voltage divider (CVD). By introducing an auxiliary resonant branch, a multi-resonant operation is established, enabling the residual inductor energy caused by component variations to be transferred to the output during the dead time with virtually eliminated hard-switching losses. Consequently, precise matching between the switching frequency and the resonant frequency is no longer mandatory. A 12 V-to-1 V/30 A GaN-based prototype was developed to validate the theoretical analysis. Experimental results demonstrate that the proposed converter maintains high efficiency under a ±10% component variation and achieves robust voltage regulation and fast transient response, making it highly suitable for high-current data center applications. Full article

Generator-side transient overvoltage may occur in DC 600 V AC–DC–AC railway power supply systems because switching-induced common-mode voltage generated by the front-end rectifier can propagate in reverse through cable distributed parameters, grounding impedance, and generator parasitic capacitance. Although AZSVPWM has been widely studied as a common-mode voltage reduction technique, its application to the suppression of generator-side reverse transient overvoltage in railway DC 600 V power supply systems has not been sufficiently investigated. In this paper, AZSVPWM is applied to the front-end active rectifier as a source-side suppression strategy. A high-frequency electromagnetic transient model is developed by considering the generator equivalent impedance, cable distributed parameters, grounding path, and generator winding-to-ground parasitic capacitance. The model is validated by comparing simulated and measured generator terminal voltages under AZSVPWM operation. Based on this model, the common-mode voltage excitation mechanism, reverse propagation path, and overvoltage suppression effect of AZSVPWM are analyzed. The results show that, compared with conventional SVPWM under identical active-rectifier conditions, AZSVPWM reduces the representative peak transient voltage at the generator terminals from 751.37 V to 557.71 V under the 8 m cable condition, corresponding to a reduction of approximately 25.77%. In addition, AZSVPWM-based active rectification improves the low-frequency voltage quality compared with conventional thyristor rectification, and the THD is estimated to decrease from approximately 14.8% to 0.8% based on the FFT spectrum. Parametric analysis further shows that AZSVPWM maintains stable suppression performance for cable lengths of 2–15 m and generator parasitic capacitances of 5–20 nF, with the maximum peak-voltage deviation caused by parasitic capacitance variation being approximately 1.15%. These results indicate that AZSVPWM provides a practical and robust source-side suppression strategy for generator-side transient overvoltage in railway DC 600 V power supply systems. Full article