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Wireless power transfer (WPT) systems have garnered significant market attention owing to their broad applicability in portable electronic devices, electric vehicles, unmanned aerial vehicles, biomedical implants, and related fields. In these systems, operating frequency and efficiency are critical factors affecting both transmission efficiency and transmission distance, making high-frequency operation an important trend for improving overall WPT performance. However, elevating the switching frequency also introduces notable challenges, including increased switching losses in power devices, limited load adaptability, and poor anti-misalignment capability, which in practice often lead to degraded system efficiency and unsatisfactory waveform quality. Accordingly, this paper proposes a high-frequency inverter power supply system capable of operating at a maximum output voltage frequency of 25 KHz. Under conditions of a 10 KHz output frequency and a 20 KΩ load, the system achieves a peak efficiency of 94.01%. A prototype was implemented through the integration of a software algorithm based on ARM Cortex-M3 core control with a hardware architecture consisting of a driving circuit, a full-bridge inverter, and a switchable filtering module. This work offers practical design insights for the development of future high-frequency, high-voltage inverter systems, while also providing valuable experimental data to support further research in this area. Full article

Large-scale integration of electric vehicle charging stations into the energy distribution network introduces highly variable power demands leading to additional voltage drops, increase in power losses, and quality degradation. Conventional mitigation strategies, including reactive power control only and multi-loop dq-axis-based controllers, often suffer from high computational complexity and limited flexibility for simultaneous active and reactive power control. This study presents a delay-modulated pulse width modulation control scheme for coordinated active and reactive power control in an energy distribution network with high penetration of electric vehicle charging load that are both time-varying and site-shifting in nature. The scheme uses a unified system comprising a solar photovoltaic array, battery storage system and a distribution STATCOM system. In this scheme, the control of active and reactive power is directly incorporated in the PWM pulse generation process by adding an adjustable delay parameter that controls the phase shift between the inverter current and the grid voltage. The proposed scheme is validated using a representative distribution feeder supplying the electric vehicle charging loads. The result illustrates that the feeder receiving end bus voltage drop is about 35% lower, the active power losses are about 40% lower, and the total harmonic distortion is at about 3%, which is within the IEEE 519 limit recommendations. Thus, the proposed control scheme is seen to be effective and computationally efficient, providing a scalable solution for real-time voltage regulation and power loss reduction. Full article

Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances ($Z_a,Z_b,Z_c$) and detection of the Negative Voltage Factor (NVF) are essential for effective condition monitoring and preventive maintenance strategies. While existing machine learning methods have demonstrated promising accuracy, they often rely on manual feature engineering, lack hierarchical representation learning, and treat impedance estimation and fault detection as isolated tasks. This paper proposes a unified Deep Multi-Task Learning framework that leverages Residual Multilayer Perceptron (ResMLP) architectures for feature-based learning and Temporal Convolutional Networks (TCNs) for end-to-end raw signal learning. Our contributions include: (1) introduction of a Multi-Head ResMLP architecture that jointly optimizes phase impedance and fault detection, achieving superior NVF accuracy (MAE = 0.0007) and a fault detection F1-score of 0.8831; (2) investigation of raw-voltage TCN models for voltage-only diagnostics, with analysis of the trade-offs between end-to-end learning and feature-based approaches; (3) extensive ablation studies demonstrating the impact of network depth, data augmentation, and training protocols on model generalization; and (4) deployment of PyTorch (v2.0.1)-based models suitable for embedded systems with real-time inference capabilities (2.3 ms per prediction). Experimental validation on a 1.1 kW three-phase motor dataset under diverse load conditions (0–10 Nm) and USV magnitudes (5–15 V) confirms the robustness and practical applicability of the proposed approach for industrial fault diagnosis and condition monitoring systems. Full article

In this work, we present a novel online acoustic sulfur hexafluoride (SF₆) monitoring system utilizing a miniaturized lithium niobate tuning fork (LNTF) sensor. The proposed system demonstrates enhanced stability and a broadband vibration–frequency response. The LNTF exhibits a fundamental resonance frequency of 32,901 Hz, and its quality factor (Q-factor) decreases from 19,700 to 18,300 as the SF₆ concentration increases at atmospheric pressure. Verification experiments at room temperature reveal a quantifiable correlation between the SF₆/N₂ mixture concentration ratio and the sensor’s mechanical impedance. Specifically, an impedance shift of 100 Ω corresponds to a concentration change of 0.0145 g/L. In air, with a signal integration time of 80 s, the measured noise voltage and current are 0.13 µV and 0.18 pA, respectively. These results underscore the potential of the LNTF as a compact, high-stability sensing platform for greenhouse gas monitoring in electrical infrastructure and industrial environments. Full article

Crosslinked polyethylene (XLPE) is a widely used cable insulation material for power cables at various voltage levels, offering excellent electrical, mechanical, and thermal stability. However, during the continuous extrusion moulding process, prolonged shear action and localized temperature accumulation can easily induce premature crosslinking. This leads to a decline in melt rheological properties and reduced processing stability, as well as having an adverse effect on the microstructure and overall performance of the formed insulation layer. This study systematically investigated the impact of shear-induced pre-crosslinking on the mechanical properties and dielectric characteristics of XLPE cable insulation materials through experimental testing methods. The experimental results demonstrate that, while premature crosslinking has a minimal effect on mechanical properties, it significantly deteriorates dielectric performance, as evidenced by increased conduction current, reduced breakdown strength, and compromised microstructural integrity. These findings suggest that, to improve the quality and reliability of XLPE cable production, engineering designs should prioritize controlling the pre-crosslinking process to ensure stable dielectric performance. 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

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms. Full article

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions. Full article

Coaxial cable plays a vital role in the wide application of telecommunications, network, and television broadcasting and other fields, with its transmission performance directly affecting signal quality and transmission efficiency. In practical applications, the length of the cable and the terminal load state of the connection often affect the stability of the signal. In order to solve this problem, we used STMicroelectronics STM32F407VET6 (STMicroelectronics, Geneva, Switzerland) as the master controller in this system, and deduced the length of the cable by analyzing the functional relationship between the length of the cable and the open circuit frequency. An open cable is regarded as a capacitor, and any two core wires are regarded as two plates of a flat capacitor. The linear relationship between open frequency and length is used to detect the length of the coaxial cable. The system then determines whether the terminal load is capacitance or resistance based on the detected frequency. If no frequency is detected, then the load is considered resistance. The system detects the resistance value of the resistor through series voltage division. If a frequency is detected, this indicates that the load is capacitance. At this time, the system uses an RC oscillation circuit composed of HGSEMI ICL8038 (Huagao Semiconductor Co., Ltd., Wuxi, China) for testing, and provides the phase shift required by the corresponding signal through the RC network, so as to detect the capacitance value. Finally, we successfully designed a coaxial cable length and terminal load detection system based on STM32F407VET6. Through this system, the user can accurately understand the length of the coaxial cable and the load of the connection terminal, which provides a reliable guarantee for the stability of signal transmission. Full article

Electrical water-bath stunning remains the predominant method used in commercial poultry slaughter worldwide yet its effectiveness and welfare implications may vary among breeds. Taiwanese red-feathered chickens differ from commercial broilers in growth rate and body composition, which may influence their response to electrical stunning. This study investigated the relationships between electrical stunning conditions, electroencephalographic (EEG) indicators of unconsciousness, behavioural reflexes, and carcass quality in Taiwanese red-feathered chickens. A total of 200 female chickens were subjected to direct-current water-bath stunning at 80, 100, 120, 140, or 160 V for 7 s. EEG activity and physical indicators of consciousness were assessed during the first 40 s after stunning, and carcass defects were evaluated post-mortem. Of the 200 birds initially evaluated, EEG data from 153 birds met predefined signal quality criteria and were included in the final analysis. EEG-defined unconsciousness was more frequent and lasted longer at higher voltages (140–160 V), although intermediate voltage levels (e.g., 120 V) did not follow a strictly linear trend. Corneal reflex and spontaneous eye blinking were strongly associated with EEG-based unconsciousness, supporting their use as practical on-site welfare indicators. At the lowest voltage (80 V), birds with higher abdominal fat percentages were more likely to be effectively stunned. In contrast, no statistically significant associations between abdominal fat percentage and stunning effectiveness were observed at 100–160 V. However, higher voltages were also associated with an increased prevalence and severity of carcass defects. These findings suggest that stunning conditions or commercial broilers may not ensure effective unconsciousness in Taiwanese red-feathered chickens. Corneal reflex and spontaneous eye blinking provide reliable, welfare-relevant indicators of unconsciousness under field conditions. Electrical settings must be carefully balanced to achieve effective stunning while minimising adverse welfare outcomes associated with excessive neuro-muscular responses. Full article

The Nd:GYSAG crystal enables multi-wavelength near-infrared laser output, with adjustable wavelengths tailored for specific application requirements, making it highly valuable for space-borne water vapor detection. This study reports, for the first time, the side-pumping characteristics and electro-optical Q-switching performance of this crystal. Using Ø3 × 73 mm and Ø4 × 73 mm crystal rods doped with 1.21 at.% Nd:GYSAG (chemical formula Nd_0.033Gd_0.93Y_1.79Sc_0.70Al_4.54O_11.99), 1060.4 nm laser output was achieved under 808 nm laser diode (LD) side-pumping at a repetition rate of 100 Hz and a pump pulse width of 250 μs. The experimental results show that the Ø4 × 73 mm rod had a higher laser threshold but exhibited significantly superior slope efficiency and maximum output power compared to the Ø3 × 73 mm rod. Using a flat–flat resonator, optimal laser performance was obtained with an output coupler transmission of 35%, yielding a slope efficiency of 37.2%. A maximum output energy of 179.4 mJ was achieved at a pump energy of 646 mJ. Thermal lensing effects were compensated using a flat–convex cavity, leading to improved laser performance and beam quality. Electro-optical Q-switching experiments were conducted using a KD*P crystal. A comparison between voltage-applied and voltage-removed Q-switching techniques revealed superior performance for the voltage-applied method. High-performance laser output was realized, achieving a maximum pulse energy of 59.6 mJ, a pulse width of 14.93 ns, and a peak power of 3.99 MW. This study provides an important foundation for the development of near-infrared laser devices based on Nd:GYSAG. Full article

GH3536 superalloy is widely used in the high-temperature components of aerospace applications for its excellent high-temperature strength and corrosion resistance. However, under such a harsh environment, surface defects can make the superalloy prone to corrosion and fatigue fractures. Therefore, it is important to eliminate surface defects through polishing. However, the existing polishing methods usually suffer from some issues such as surface integrity damage, low efficiency, and poor environmental sustainability. More importantly, these methods fail to account for the requirement of surface roughness below 0.05 μm in some high-precision aerospace components. Herein, the plasma electrolytic polishing (PEP) of GH3536 superalloy is systematically investigated and optimized through single-factor experiments and response surface methodology (RSM). A minimum surface roughness Ra of 0.044 μm with a mirror-like surface was achieved at a voltage of 303.8 V, electrolyte temperature of 66.2 °C, polishing time of 5 min, and submersion depth of 7.5 cm. At the same optimized condition, the material removal rate was 59.12 mg·min⁻¹. After polishing, the surface composition of GH3536 superalloy varied negligibly, while its corrosion resistance improved markedly, with a 53.72% increase in polarization resistance and a 43.46% decrease in corrosion current density. Meanwhile, the microhardness slightly decreased due to the removal of the work-hardened layer and the compressive residual stress exhibited a more uniform distribution across the surface, contributing to improved near-surface mechanical stability. This study establishes an optimized PEP parameter for improving the surface quality of GH3536 superalloy, offering a practical method for the precision finishing of aerospace-grade superalloy components. Full article

Mini-grids are increasingly deployed to expand rural electrification in developing countries, yet evidence on service-quality performance remains uneven. This systematic review synthesises empirical evidence from 22 peer-reviewed studies (2005–2025) on rural mini-grid performance across six energy-access indicators: electrification rate, availability of supply, hours of supply, affordability, reliability, and consistency (power quality). Using PRISMA-guided database searches in Scopus and Web of Science, 138 records were identified; following de-duplication and screening, 22 studies met the inclusion criteria. The evidence base is concentrated in Africa and Asia, and most studies adopt mixed-methods approaches combining household- and/or enterprise-level evidence with system or operational data. Across indicators, electrification outcomes are frequently positive but reported using heterogeneous metrics, often relying on connection counts rather than population-referenced rates (10/22 studies report electrification outcomes). Service availability and hours of supply vary widely, ranging from evening-only provision (~5 h/day) to near-continuous service (24 h/day), with several studies documenting demand–capacity mismatch and load shedding (9/22 quantify availability; 12/22 quantify hours). Affordability is most frequently reported (16/22 studies), spanning substantial household cost reductions in some settings to high tariffs that constrain uptake in remote contexts. Reliability is seldom quantified using extractable outage/downtime metrics (4/22 studies). No study reports standardised voltage/frequency power-quality measures; only proxy evidence relates to consistency, leaving power quality as a major evidence gap. Mini-grids can deliver meaningful improvements in rural electricity access, but the literature remains constrained by inconsistent indicator definitions, limited standardised reliability/power-quality measurement, and short monitoring horizons. Future research and regulation should prioritise harmonised service-quality metrics and longer-term, field-based performance evaluation. Full article

To address the unobservability of distribution networks caused by insufficient coverage of measurement terminals as well as communication failures and missing data, and to cope with operating-state fluctuations induced by distributed generation integration and external environmental disturbances, this paper proposes an integrated state-estimation and voltage-regulation strategy that combines distribution-network-partitioning-based optimal PMU placement with pseudo-measurement construction using power transfer distribution factors (PTDFs). First, nodal reactive-power sensitivity information is derived from the power-flow Jacobian matrix, and an improved modularity function is employed to obtain the optimal partitioning of the distribution network, based on which PMUs are deployed at partition boundary buses. Second, PTDF-based power pseudo-measurements are constructed for unobservable buses and incorporated into the measurement model via a measurement transformation; a weighted least-squares method is then adopted to achieve system-wide state estimation. Finally, the estimated voltage states are fed into flexible voltage-regulation devices to enable fast and continuous voltage adjustment across buses. Case studies on the IEEE 33-bus system demonstrate that the proposed method effectively improves voltage quality. Full article

The intermittent nature of photovoltaic (PV) energy, especially under nonlinear and unbalanced loading situations, has made it more difficult to ensure steady operation as it is increasingly integrated into modern power systems. The Power Quality (PQ) issues cause severe degradation of both system performance and device lifetime. A novel Neural Power Quality Network (NeuPQ-Net) controlled Unified Power Quality Conditioner (UPQC) combined with a Quasi Z-Source Lift (QZSL) converter for PV applications is presented in this research as a novel solution for addressing these issues. The QZSL converter, which is controlled by a Maximum Power Point Tracking (MPPT) algorithm based on Perturb and Observe (P&O), increases the PV source voltage to the necessary DC-link level. A Zebra Optimisation Algorithm tuned PI (ZOA-PI) controller continually adjusts PI gains for quick and accurate regulation, ensuring steady DC-link voltage. Unlike conventional Synchronous Reference Frame (SRF) or Decoupled Double Synchronous Reference Frame (DDSRF)-based reference generation, the proposed NeuPQ-Net operates directly in the abc domain, eliminating Phase-Locked Loop (PLL) dependency and reducing computational complexity. Simulation and hardware prototype validations demonstrate that the proposed system achieves significant improvements in PQ indices, including reduced Total Harmonic Distortion (THD), faster response to transients, and enhanced voltage regulation, while complying with IEEE-519 standards. Full article