High-frequency transformer cores increasingly suffer from vibration and acoustic noise caused by the magnetostriction of nanocrystalline ribbons; accurate evaluation of magnetic hysteresis and magnetostriction is therefore essential. To address the poor fitting accuracy and low identification efficiency of existing models under high-frequency excitation, this study first develops an improved coupled hysteresis-magnetostriction model. Starting from the conventional static Jiles-Atherton (J-A) framework, eddy-current and excess-loss terms are incorporated to capture frequency effects, while a dissipation factor and a memory-coupling factor enhance accuracy near loop turning points. A first-order inertial differential term is further embedded in the quadratic domain-rotation magnetostriction model to reproduce hysteretic lag, yielding a unified dynamic coupling formulation. Based on the proposed model, an improved single-objective parameter-identification scheme is introduced. A normalized composite objective function that simultaneously considers magnetic-field and magnetostriction errors is minimized using a single-objective particle-swarm optimization (PSO) algorithm. Relative to a multi-objective PSO, this strategy reduces the root-mean-square errors of magnetic field and magnetostriction by 32.76% and 22.2%, respectively, while shortening computation time by 64.32%. Finally, a rapid identification method across magnetic-flux densities is presented. Intrinsic material parameters are fixed, and the remaining parameters are expressed as polynomial functions of flux density, enabling fast prediction and adaptive modeling under varying operating conditions. The proposed modeling and identification techniques provide a reliable theoretical and methodological basis for high-frequency transformer-core vibration analysis and soft-magnetic-material performance evaluation.

With the advancement of the "dual carbon" goal, decarbonization in the power industry has become a key focus in achieving a zero-carbon society. Among the critical challenges, reducing carbon emissions from distribution networks is an essential issue in the low-carbon operation of power systems. Therefore, this paper develops a low-carbon economic dispatch model for distribution networks, based on the theory of carbon emission flow and demand response. Firstly, the carbon potential distribution of the distribution network is analyzed based on the theory of carbon emission flow, and the dynamic carbon emission factor for the distribution network is calculated. Secondly, a combined demand response model, including both price-based and incentive-based approaches, is constructed. By integrating the dynamic carbon emission factor, time-of-use electricity prices, and certain incentive subsidy policies, the model encourages users to actively adjust their electricity consumption behavior. Finally, simulations are conducted using the modified IEEE 33-bus system for comparison. Finally, case study simulations are conducted using the modified IEEE 33-bus system. The analysis demonstrates that the proposed model can significantly lower carbon emissions in the distribution network, while simultaneously reducing operational costs and enhancing the stability of the system.

To address the instability and insufficient detection accuracy of Density Peak Clustering caused by manual setting of the cutoff distance, this study proposes an anomaly detection method combining Improved Grey Wolf Optimization and DPC. An improved chaotic mapping is employed to uniformly initialize the grey wolf population, and a nonlinear decaying convergence factor is designed to dynamically balance global exploration and local exploitation. The Davis–Bouldin index is used as the fitness function to automatically optimize key DPC parameters, achieving parameter adaptivity. Case studies on electricity consumption datasets from multiple regions and comparative experiments against mainstream anomaly detection algorithms show a notable improvement in AUC relative to the baseline, validating the method’s significant advantages in detection accuracy and stability.

The thermal network models play a core role in the thermal design, thermal management and reliability assessment of electronic devices, which have evolved from classical linear models to nonlinear thermal network models that are more consistent with physical reality. In the practical application of nonlinear thermal models, the accurate determination of thermal parameters is a key factor to ensure the model accuracy. However, the traditional model parameter extraction method based on transient thermal impedance has strict requirements on the stability of working conditions, making it difficult to be applied to power semiconductor devices with variable working conditions in actual operation. To address this, this study proposes a parameter identification method for nonlinear thermal network models of power devices. The method constructs an objective function based on the nonlinear thermal network model and uses the Particle Swarm Optimization (PSO) algorithm for global optimization to obtain the optimal solution of the model identification parameters. Experimental results verify that the method can accurately extract thermal parameters that are difficult to calculate theoretically, and it does not rely on constant working conditions, making it suitable for online use under actual variable working conditions. This provides a new approach to realize the reliability of junction temperature estimation engineering for power semiconductor devices.

To address the control performance degradation of high-speed permanent magnet synchronous motor (HSPMSM) caused by time-varying parameters during operation, this paper proposes an adaptive control strategy based on online multi-parameter identification. The strategy establishes a hybrid parameter identification framework that integrates model reference adaptive system (MRAS) with recursive least squares (RLS), enabling real-time high-precision estimation of critical parameters including stator resistance, d/q-axis inductances, and permanent magnet flux linkage. Based on the identification results, the parameters of both speed and current loop controllers are dynamically adjusted. To further enhance the convergence speed and estimation accuracy of parameter identification, an adaptive forgetting factor mechanism for RLS is proposed. Experimental results demonstrate that compared with conventional control strategies, when significant changes occur in load conditions and motor parameters, the proposed method achieves rapid and accurate motor parameter identification while effectively suppressing speed fluctuations and torque ripples induced by parameter mismatch, thereby significantly improving the control robustness under time-varying operating conditions.

The medium-voltage cable joint is a critical component that determines the overall safety and reliability of cable systems, with bolt compression serving as the key process influencing joint quality. In conventional cable bolt connection design, the final configuration is often determined empirically based on the designer’s experience, lacking a systematic and scientific optimization approach. In this study, a three-dimensional finite element model of the cable bolt was developed to analyze the stress distribution and fatigue life characteristics during the compression process. By integrating MATLAB with finite element co-simulation, a quantitative relationship among bolt size, position, and compression performance was established. Taking stress life, maximum stress, and average stress as the three optimization objectives, the NSGA-II algorithm was employed to obtain a Pareto-optimal solution set, and the TOPSIS method was subsequently applied to identify the optimal structural configuration. The results demonstrate that rational optimization of bolt dimensions and positioning can approximately double the average contact stress and extend the fatigue life by about 1.5 times. The optimized design effectively reduces contact resistance while enhancing mechanical integrity and electrical stability. Both simulation and experimental results confirm that the proposed method significantly improves the safety and reliability of cable joints, providing theoretical and technical guidance for the engineering optimization of bolted compression structures.

Moisture ingress is one of the key factors that leads to the degradation of silicone oil performance and induces partial discharge, posing a serious threat to the operational safety of power equipment. This study investigates the discharge behavior of silicone oil under different moisture content conditions by controlling exposure durations (0, 1, 2, 3, and 5 days) to regulate its water content. A systematic analysis is conducted on how varying moisture levels influence the partial discharge characteristics of typical insulation defects within cable terminals. Two types of discharge models—needle-plate and surface discharge structures—are constructed to simulate typical defects. Partial discharge signals are collected and analyzed to quantify discharge magnitude, frequency, and phase-resolved characteristics. The results show that silicone oil becomes saturated with moisture after approximately five days of exposure, with increased moisture content significantly intensifying partial discharge activity. Under the needle-plate defect model, the discharge magnitude at the same voltage level increased by 478%, 586%, and 546% compared to new oil; under the surface defect model, the increases were 226%, 379%, and 518%, respectively. Furthermore, under identical conditions, the maximum discharge quantity induced by the surface defect was at least 2.3 times greater than that of the needle-plate defect.

To address prevalent challenges of inertia deficiency and stability fragility in renewable energy microgrids integrated with weak AC grids across remote regions in midwestern China, this study proposes an energy storage-based flexible interconnection system employing grid-forming converters. The system delivers dual-port grid-forming support to both renewable microgrids and weak end-of-grid networks. Considering the impact of fault current limiting control on system’s transient stability, the power characteristic models for the interconnection device under d-axis prioritized, q-axis prioritized, and phase prioritized current limiters, are established. Through active power-power angle curve analysis, the impact of current limiters to transient stability is comparatively evaluated, when implemented with virtual admittance voltage loop and vector current loop control. Key findings reveal that, the grid-forming converter based on virtual synchronous generator and virtual admittance inner-loop control can achieve enhanced transient stability margins under q-axis prioritized current limiter. Validation via PLECS simulation results confirm both the accuracy of the power characteristic models and the correctness of theoretical conclusions.

To address the issue of tower vibration and nonlinear dynamics during large-scale wind turbine operation, this paper develops a damping control algorithm for the tower based on a resonant compensation linear active disturbance rejection controller. The research proceeds as follows: Firstly, the force and motion equations of the tower are examined, and the dynamic characteristics of tower vibration, along with key factors influencing these dynamics, are identified. Secondly, by designing pitch damping control algorithms using a second-order linear active disturbance rejection controller framework, the impact of the proposed control strategy on the system's damping ratio is analyzed, and corresponding control parameters are determined to mitigate front-rear and left-right vibrations of the tower. Finally, taking the NREL 5 MW wind turbine as a case study, verification results demonstrate that the proposed method can effectively attenuate tower vibrations and load fluctuations, and enhance the control algorithm's robustness and disturbance rejection capabilities.

Although gas insulated switchgear (GIS) is ready for operation after passing the AC withstand voltage test, it may still malfunction due to insulation defects in actual operation. Engineering applications show that under the instantaneous impact of switching overvoltage, the internal potential defects of GIS equipment may be activated and cause partial discharge. To explore this working condition, this study constructed a superimposed experimental platform of alternating voltage and switching impulse voltage, and analyzed the evolution law of partial discharge characteristics of linear metal particles of different sizes in SF6 gas. The result shows that the switching impulse voltage has a significant excitation effect on linear metal particles under the action of power frequency voltage. Moreover, the particle discharge does not occur instantaneously but has a discharge hysteresis time Δt. The discharge hysteresis time of linear particles of different sizes is different, and the intensity and discharge frequency of partial discharge increase with the increase of the diameter of linear particles. The research work of this paper has practical engineering significance for exploring the internal mechanism of metal particle discharge and development under impulse voltage and ensuring the safe operation of the power system.

Currently, when using electronic ultrasonic sensors to monitor the partial discharge of power equipment, the minimum detectable strain value has not yet been established through experimental means as a quantitative standard. To conduct a quantitative study on the minimum detectable strain value of the electronic ultrasonic sensor, this paper proposes a quantitative analysis method for the spatial acoustic field strain of power equipment partial discharge. Firstly, a needle plate discharge test platform is set up, and the ultrasonic signals in the space are measured using the electronic ultrasonic sensor. The speed of the diaphragm is calculated through the sensitivity coefficient. Secondly, the same discharge model is established in the finite element simulation software, and the equivalent speed of the spatial acoustic field is used to explore the amplitude range of the partial discharge ultrasonic point sound source corresponding to the experiment, thereby establishing a function relationship between the partial discharge quantity and the amplitude of the partial discharge ultrasonic point sound source. Finally, based on this function relationship, the quantitative analysis of the acoustic field strain of the partial discharge sound field is completed. The results show that the actual amplitude of the partial discharge ultrasonic point sound source, which is usually set as 1 in existing literature, is actually at the order of 10-7, and this amplitude has a linear relationship with the partial discharge quantity. The acoustic field strain of the partial discharge can be calculated in the simulation based on this linear relationship, and thus the sensitivity parameters that the electronic ultrasonic sensor should meet can be obtained.

Existing load curve clustering methods frequently fail to adequately capture the diurnal fluctuations and intraday lag effects present in power load data, leading to clusters with limited accuracy and interpretability. To overcome these limitations, this paper proposes a two-stage clustering approach integrating the STL (Seasonal-Trend decomposition using Loess) algorithm with an enhanced K-Shape method. Initially, the STL algorithm is applied to decompose the load data and remove the trend component, resulting in a detrended sequence. This sequence then undergoes a two-stage clustering process. In the first stage, the K-means++ algorithm is employed, where the optimal number of clusters is determined using the elbow method. The second stage involves an improved K-Shape procedure, incorporating unbiased estimators and coefficient normalization with compensation, yielding unbiased normalized coefficients. This enhancement facilitates the extraction of the Optimal Displacement Partition (ODP) and enables the identification of cluster centroids through minimized distance measures. Experiments conducted on real-world power load data demonstrate that the proposed method outperforms the STL-K-means++ baseline, improving the SIL index by 10.07% to 48.99% and reducing the DBI index by 6.44% to 22.24%.

With the development of hydraulic turbine generators towards higher rated voltages and larger single-unit capacities, higher requirements are placed on their insulation systems and corona protection performance. The electric field at the generator end is concentrated and unevenly distributed, making it a weak link prone to corona discharge. The currently adopted three-stage corona protection structure can meet the insulation performance requirements of generators under conventional operating conditions. However, with the increase in altitude, the influence of environmental parameters becomes gradually significant, necessitating a higher level of insulation corona protection capability to ensure the safe and stable operation of the generator set.In this paper, the Nelder-Mead algorithm is used to perform finite element electromagnetic simulations on the stator bars of a hydropower station's hydraulic turbine generator, optimizing the original three-stage corona protection structure and improving the generator's insulation corona protection capability. The results show that using the Nelder-Mead algorithm to optimize the corona protection materials and structure of the bars is fast in calculation speed and low in computational cost. Compared with the initial state, the optimized scheme reduces the maximum electric field strength on the bar surface by 20% under the rated operating voltage, and the optimized scheme performs well under other operating voltages. The optimal design method proposed in this paper can provide a reference for the insulation optimization design of high-voltage and large-capacity hydraulic turbine generators.

Space Vector Pulse Width Modulation (SVPWM) is widely employed in T-type neutral-point clamped (T-NPC) three-level inverters to achieve high DC voltage utilization and low total harmonic distortion (THD). However, conventional SVPWM struggles to achieve active neutral-point potential balancing under full operating conditions, compromising system safety. While the emergence of Virtual SVPWM (VSVPWM) technology effectively addresses the neutral-point potential balancing issue, it introduces significant common-mode voltage (CMV) during vector synthesis. This CMV is converted into common-mode current (CMC) through parasitic capacitances, increasing system losses and posing risks to equipment reliability. Although improved LCL-filter-based three-level inverter topologies can suppress high-frequency CMV, they generate substantial circulating line currents, thereby amplifying the total CMC. To resolve these challenges, this paper proposes an Optimized VSVPWM (OVSVPWM) strategy. By optimizing vector sequence selection and zero-vector redistribution, the proposed strategy significantly reduces both CMV amplitude and circulating line currents, effectively suppressing CMC. Experimental results demonstrate that, compared to conventional VSVPWM, the improved strategy reduces the common-mode current amplitude by over 50%, conclusively validating its efficacy and superiority.

Based on the analysis of the electrical quantity of the new energy access system, the adaptability of protective auxiliary elements used in the traditional AC line, such as the direction element and phase selection element, differential protection and distance protection, is evaluated, and the adaptive boundaries of the above protection are given, and the adaptability evaluation method is verified in combination with the topology of Central China Power Grid. Finally, PSCAD/EMTDC simulation is used to verify the applicability of the proposed adaptability analysis method to Central China Power Grid, and the possible problems of traditional AC protection in Central China Power Grid are pointed out, which can provide theoretical guidance for the research on the adaptability of relay protection in Central China Power Grid.

The stochastic and intermittent nature of wind power generation may lead to the exhaustion of system regulation resources, thereby causing wind curtailment and load shedding phenomena. Traditional chance-constrained economic dispatch models in power systems struggle to effectively handle wind curtailment and load shedding. To address this challenge, this paper proposes a chance-constrained economic dispatch method considering wind curtailment and load shedding for power systems. First, a Gaussian mixture model is employed to generate stochastic wind power scenarios that account for wind generation uncertainty. Second, a CCED model is established, which incorporates reserve shortage factors to formulate coordinated wind curtailment and load shedding strategies. Energy storage systems are integrated into the model to reduce wind curtailment while ensuring economic system operation. Furthermore, a chance constraint reformulation method based on conditional value-at-risk (CVaR) and duality theory is proposed. The inherent bilinear problem is resolved through piecewise linearization techniques, transforming the original optimization problem into a mixed-integer linear programming formulation. Finally, case studies demonstrate the feasibility and cost-effectiveness of the proposed methodology, verifying its capability to balance operational reliability and economic objectives in wind-integrated power systems.

Superconducting materials possess excellent properties such as zero resistance, diamagnetism, and the electron tunneling effect. By using them as the materials for the rotor or stator coils of motors, high-temperature superconducting motors (HTS motors) can be fabricated. Relying on their technical advantages of high efficiency, low loss, and high-power density, HTS motors have demonstrated important engineering application values in the fields of megawatt-level power equipment, such as large-scale wind turbine generator systems, marine vessel electric propulsion, and electric aviation propulsion systems. This paper first provides an overview of the application scenarios of HTS motors, including HTS motors, HTS generators, HTS linear motors, HTS motors with special structures, and HTS synchronous condensers, and introduces the research progress of HTS motors both at home and abroad. Secondly, it elaborates and summarizes the key technologies of HTS motors, including high-temperature superconducting materials, cryogenic cooling technology, and quench protection. Finally, it explores the feasible optimization approaches of HTS motors in aspects such as the motor structure, superconducting materials, cooling technology, and quench protection mechanism.

SiC MOSFET devices face chip aging issues caused by gate oxide degradation, with their electrical characteristics evolving during the aging process, which critically impacts key parameters such as power loss and junction temperature. To evaluate the operational performance of SiC MOSFETs and associated power electronics systems during aging, it is essential to clarify the device aging mechanisms and develop simulation models that characterize device behavior across various aging states. This paper investigates the threshold voltage degradation mechanisms of SiC MOSFETs under AC gate stress and establishes a corresponding characterization model. A device simulation model is proposed to capture the impact of threshold voltage variations on electrical characteristics. The effects of gate voltage amplitude, frequency, and temperature on threshold voltage drift are systematically analyzed through AC gate bias accelerated aging tests, enabling the development of a degradation model for threshold voltage. Key physical processes governing channel current are modeled, with threshold voltage serving as the aging indicator, to derive a channel current model reflecting the influence of aging on static characteristics. This model is integrated with nonlinear capacitance models to construct a comprehensive device simulation framework. Finally, the accuracy of the proposed model in characterizing static properties across aging states is validated through simulation-measurement comparisons. The model's capability to simulate switching behavior before and after aging is further verified via double-pulse tests.

Currently, the parallel use of silicon carbide (SiC) MOSFETs is the mainstream solution for high current carrying. However, under long time-domain service conditions, the accumulation of time-varying non-uniform electro-thermal stresses will lead to different aging trajectories of the parallel devices, and this aging variability will trigger a positive feedback mechanism with different degrees of parameter drift - deterioration of parallel equalization of currents - local overstress - and accelerated parameter drift, which poses a threat to the reliability of the system. To this end, this paper proposes a parallel current sharing regulation method for SiC MOSFETs under aging degree difference based on driving voltage compensation, aiming to achieve parallel dynamic current sharing under threshold voltage mismatch. First, a double-pulse test (DPT) platform and a Boost experimental platform for parallel connection of two tubes were built to deeply investigate the coupling effect between the aging degree difference and the parallel current sharing; Then, the mathematical relationship between threshold voltage dispersion and turn-on voltage regulation is theoretically derived, and an current sharing regulation strategy based on driving voltage compensation is proposed and experimentally verified under different aging degrees. The results of this paper help to improve the reliability of SiC MOSFETs in parallel applications.

Household all vanadium flow battery energy storage systems for photovoltaic-storage applications are difficult to assess the energy loss economics due to the low average system efficiency under operations such as intermittent power supply, hot standby, and multiple applications, and the unbalanced distribution of energy consumption. This paper proposes a dynamic assessment method for energy loss of household all vanadium batteries, which includes loss calculation methods for flow resistance, battery polarization, ion crossing, branch current and auxiliary thermal management, and takes 10 kW vanadium flow battery equipment as a test research object to analyze the distribution of each energy loss parameter of the battery over time for multiple charging and discharging experiments in the constant-voltage, constant-power charging and discharging modes under photovoltaic access and to establish a loss assessment The probability distribution function of the energy loss rate and SOC/SOD is established to evaluate the energy loss interval during the long charge/discharge cycle of the household system. The results show that the energy loss of the household system varies linearly with SOC between 26.6% and 40.2%, and the flow resistance loss accounts for 6%~7% of the interval in the long cycle operation, with the largest value near SOC=0.25; the battery polarization loss accounts for 8%~9% of the interval, with the largest value near SOC=0.85; the ionic cross-loss accounts for 1%~2% of the interval, with the largest value at the end of the charging and discharging period when SOC=0.75; and the branch circuit loss accounts for 1%~2% of the interval. The loss value is largest near SOC=0.75 at the end of charging and discharging period; the branch circuit loss accounts for 4%~5% of the interval, and the loss value is largest near SOC=0.85; and the loss of auxiliary thermal management equipment accounts for 5%~6% of the interval in the high temperature environment in summer, and the loss value is largest near SOC=0.25. This study provides experimental guidance for the operation optimization of the optical storage and charging multi-scenario vanadium flow battery energy storage system, and the experimental data provide effective support for the improvement of system parameter design.

Hydropower is a clean energy source that can be able to effectively reduce fossil energy consumption and play an important role in carbon emission reduction and resource conservation. However, the interaction between the hydraulic turbine and the power grid may have the risk of broadband oscillation. The impedance analysis method can effectively simplify the process of stability analysis, its physical meaning is clear, and the model is easy to systematically extend. Therefore, based on the impedance analysis method, this paper, the hydropower unit is usually divided into three parts: synchronous generator, hydraulic turbine and governor, respectively, establishes its small-signal linearization equations and constructs the impedance model under the dq coordinate system of the hydropower unit, and establishes the sequence impedance model through its mathematical conversion relationship with the sequence impedance model. This method is based on the modular impedance model, and when the system structure and parameters are changed, only the transfer functions and parameters of the corresponding modules need to be changed without re-establishing the system model. Secondly, the frequency scanning method is used to validate the established impedance model by scanning, which proves the accuracy of the impedance model established in this paper. Finally, the stability analysis of the grid-connected system of hydraulic turbine unit and new energy unit based on impedance method. The results show the applicability of the established impedance model of hydropower unit in oscillation analysis.

In LCC-HVDC converter stations, the inrush current generated by converter transformers during no-load commissioning often causes the tripping of AC filter protection. However, current research on the impact of inrush current during no-load commissioning on AC filter protection is insufficient and has not clearly identified the reasons for protection tripping. This paper analyzes the time-frequency characteristics of inrush current, the impedance and shunting characteristics of the AC filter, combines these with the operating principles of the AC filter protection device, quantifies the impact of inrush current on AC filters, and clarifies that the greatest threat of inrush current to AC filter protection is the overloading of resistors in AC filters caused by the third harmonic. The study is verified by using a recorded inrush current waveform from ±800 kV Kunbei Converter Station and a simulation analysis was conducted on the factors affecting the overload of the AC filter resistor. The results show that the slowly decaying sympathetic inrush current can directly lead to resistor overload, and ±800 kV Kunbei Station can withstand an inrush current peak of approximately 6 300~6 500 A.

The temperature control scheme based on the high-frequency induction heating is proposed for the production wells of drilling type in-situ pyrolysis of tar-rich coal. The key of this scheme is to set 4 temperature control wells around each production well, and the heating device in each of the temperature control well is composed of multiple heating units in parallel. The heating unit includes high-frequency induction heating circuit, sealing heat-insulation device and start-stop control strategy. The heating circuit consists of the step-down transformer, three-phase uncontrolled rectifier, high-frequency inverter, and heating coil. The sealing heat-insulation device consists of multi-stage sealed chambers, in which the high-speed circulating nitrogen is loaded to bring the heat generated by the element to the ground cooling system, thus to maintain the low temperature in the sealed chamber. Based on the characteristics of the large-time constant of the temperature control system, a centralized start-stop control strategy based on temperature feedback of the producing well is proposed, and the simulation analyses verify the feasibility of the design scheme.

In modern power systems, the performance monitoring of high-voltage line insulators is crucial for ensuring the safe operation of the power grid. Traditional monitoring methods have limitations in safety and efficiency, which are difficult to meet the needs of modern power grids for intelligent and digital transformation. This paper proposes a lightweight intelligent registration algorithm based on the improved YOLOv5 and Super-Point cascade, specifically targeting the nonlinear distortion and large parallax image registration problems of line insulators. The algorithm significantly improves the performance of image registration by introducing technologies such as GhostBottleneck structure, group convolution, and anchor frame edge feature removal, achieving efficient multi-view image registration with a mutual information value higher than 0.8. Compared with traditional image registration algorithms such as SIFT, SURF, and ORB, this method shows significant advantages in training efficiency, registration accuracy, and anti-interference capability. The study not only improves the efficiency and accuracy of multi-view image information fusion but also provides important technical support for multi-view imaging information fusion applications carried by drones or robots, with a broad application prospect and significant practical significance.

Compared with the traditional grid-following energy storage converter (GFL-ESC), the grid-forming energy storage converter (GFM-ESC) can provide voltage and frequency support for the power grid, which can significantly improve the system stability under short-circuit fault scenarios, and has attracted wide attention in recent years. Consequently, GFM-ESC have attracted great attention in recent years. When an asymmetrical short circuit fault occurs in the power grid, GFM-ESC system will experience transient instability, but there are few researches on this problem. In this paper, the GFM-ESC system is investigated under asymmetrical short-circuit fault conditions. In addition, a dual-loop control structure model of positive- and negative-sequence voltage and current is established for the GFM-ESC system under asymmetrical short-circuit fault conditions considering the sequential switching characteristics. Based on the symmetrical component method, the composite sequence network of the system is obtained under asymmetrical short-circuit fault conditions, and positive- and negative-sequence power-angle characteristic curves are analyzed. The influence law of system parameters on the transient synchronous stability of positive- and negative-sequence systems is quantitatively analyzed through the equal area criterion. Finally, the correctness of the theoretical analysis is verified by hardware-in-the-loop experiments.

When an asymmetric fault occurs in the power grid, the grid-connected system of Grid-Forming Converters (GFM) will encounter power quality issues such as over-limit fault currents, DC voltage fluctuations, and triple-frequency harmonic currents. To ensure that the power quality of the GFM output during the fault meets the grid-connection requirements and to provide a certain amount of reactive power support for the system, this paper proposes a GFM power coordination control strategy under asymmetric grid faults. Firstly, based on the GFM grid-connected topology, the analytical expressions and constraint conditions of the GFM active power fluctuation amplitude and the maximum phase current under asymmetric faults are derived, and the safe operating region of the GFM is obtained. Subsequently, a reactive power-prioritized GFM power coordination control strategy is proposed based on the safe operating region, which precisely regulates the active power output on the premise of meeting the reactive power support requirements of the system. Finally, the accuracy of the theoretical analysis and the effectiveness of the power control strategy in this paper are verified based on the MATLAB/Simulink simulation platform.

252 kV single-break vacuum circuit breaker is known as the "crown jewel" in the field of environmentally friendly high-voltage switchgear. The vacuum interrupter is the core component of vacuum circuit breaker, and mastering the temperature distribution characteristics of vacuum interrupter is crucial for its structural optimization design and safe operation. This paper carries out the research on the temperature rise characteristics of 252 kV single-break vacuum interrupter and its contact temperature calculation method, based on the electromagnetic-temperature simulation method to study the correlation between the dynamic temperature rise of vacuum interrupter contacts and the load current and ambient temperature, and verify the validity of the simulation method and the contact temperature calculation method. The results show that the highest temperature of the interrupter chamber occurs between the movable and static contact pieces, and the temperature rise of the contact pieces is 70 K at 1.1 times the rated current; the contact temperature of the interrupter shows a saturation-type exponential law increasing trend with the growth of the through-current time, and the ambient temperature mainly affects the size of the contact steady state temperature value; This paper proposes a mathematical model to quantitatively calculate the contact temperature based on load current and ambient temperature, and the error between the measured and simulated temperatures of the 252 kV vacuum interrupter conductive rods and the main shielding cover key point temperatures rise obtained based on the high-current temperature rise test platform is less than 2 K; This paper provides a reference for grasping the temperature characteristics of the vacuum interrupter chamber of the 252 kV environmentally friendly switch.

As the core equipment of the power system, the temperature rise of the transformer is an important representation of its health status. The internal temperature monitoring of the transformer is of great significance to the safe and stable operation of the power grid. Aiming at the problem that it is difficult to arrange distributed sensors inside the transformer for direct temperature monitoring, this paper proposes an inverse method for internal temperature based on multi-point inverse transformation mapping of transformer surface temperature. This method constructs the matrix inverse problem model of the internal and external temperature mapping of the transformer, solves the inverse matrix through the electromagnetic heat flow coupling finite element simulation data of the transformer, and then realizes the internal temperature inversion by combining the temperature data of the outer measuring points. In this paper, the validity of the multi-point inverse transformation model is verified by multi-physical field simulation and the D-800 / 35 scaled transformer model. The results show that the average error of the internal temperature inversion error of the transformer is less than 1 K under the condition of less temperature input of the measuring point on the surface of the transformer, which has higher accuracy and lower computational complexity. This method can inverse the internal temperature of the transformer quickly and without sensor intrusion, which can provide theoretical support and technical support for the temperature monitoring and fault diagnosis of the transformer.

With the development of power distribution systems and the acceleration of urbanization, power cable transmission is advancing toward higher loads, safety, and reliability. The cable branch box, as an essential component of the distribution network, houses the 10 kV T-type cable joint, which serves as a core element within the box. The operational state of the T-type cable joint directly affects the safety and reliability of the power system. Based on the finite element analysis method, this study employs COMSOL Multiphysics software to establish a three-dimensional electro-thermal-fluid coupling model of the cable branch box and its internal T-type cable joint. The work investigates the effects of current load, ambient temperature, airflow velocity, and the number of ventilation openings on the temperature field distribution of the joint. The results reveal that variations in current load lead to a nonlinear increase in the joint temperature, the rise in ambient temperature significantly amplifies the temperature increase of the joint, and ventilation conditions present a more pronounced effect on the temperature field distribution. Specifically, airflow velocity within a certain range greatly enhances cooling performance, but the cooling effect saturates beyond a threshold. Additionally, increasing the number of ventilation openings improves cooling efficiency, especially under high-load operating conditions. This study provides theoretical support for the optimization of cooling systems in cable branch boxes and offers valuable insights for the operational management and fault prevention of cables and T-type cable joints.

With the development of UHV DC transmission technology, DC measurement equipment represented by fiber-optic current transformers, DC voltage dividers, and zero-flux transformers have been applied in large numbers. However, DC measurement data may have accuracy and drift problems due to equipment quality defects, poor safety processes, and long-term operation aging. The self-test logic of DC measurement equipment cannot fundamentally avoid the influence of bad data, and the existing power system bad data identification methods cannot meet the requirements of DC control and protection reliability and rapidity. To this end, the characteristics of bad data of DC measurement devices are analyzed, a training method for generating bad data of DC measurement devices is proposed, and a training method for bad data identification model of DC measurement devices is proposed, so as to propose a bad data identification method for DC transmission system based on the improvement of Random Forest in Spark, and the validity of the proposed method is verified by the arithmetic examples. The method introduces Spark architecture to improve the speed of DC measurement equipment abnormal data identification, and uses generative algorithms to improve the data distribution and enhance the accuracy of DC measurement data bad data identification.

Phase-shifting transformers (PSTs) effectively control power flow distribution within an electrical grid by adjusting and regulating parameters such as voltage magnitude, phase angle, and line reactance at nodes and transmission lines. This paper focuses on a symmetrical dual-core phase-shifting transformer. Through phasor analysis and calculations of its unique topological structure, the phase shift angle and equivalent impedance under steady-state operation are determined. A Simulink simulation model of a 230 kV / 370 MVA phase-shifting transformer was developed to obtain technical parameter values under different tap positions, verifying its phase-shifting functionality. Furthermore, the current characteristics under inter-turn short-circuit conditions were analyzed. The research results indicate that the equivalent impedance of the phase-shifting transformer is minimal at the 0 tap position and increases as the tap position moves away from zero (either increasing or decreasing). Compared to a conventional two-winding transformer, when an inter-turn short circuit occurs on the primary side of the dual-winding phase-shifting transformer, the primary current of the phase-shifting transformer increases and shows an upward trend with the increase of the short-circuit turns, while the secondary current remains basically unchanged. The research conclusions can guide the condition analysis and assessment of phase-shifting transformers.

To enhance the transmission capacity in power system scheduling computations, this paper proposes a multi-scenario unit commitment model considering dynamic line capacities, accompanied by a Lagrangian relaxation solving algorithm tailored for the model. Initially, leveraging quantile regression principles, a data-driven dynamic line capacity augmentation model is constructed based on historical environmental parameter data. Next, the dynamic transmission line model is embedded into a multi-scenario unit commitment framework, thereby forming a unit commitment model that accounts for fluctuating line capacities. Lastly, system constraints within the model are relaxed and decomposed into multiple subproblems, which are then resolved through an iterative algorithm until convergence is achieved. The proposed unit commitment model and its corresponding algorithm are applied to IEEE-118 and IEEE-300 test systems, validating the efficacy and feasibility of both the model and the algorithm.

Aging failure of transformer bushing seals under thermal oxidation and compression is a key cause of oil leakage accidents. To explore their aging mechanism, multi-layer compression tests were conducted on NBR and FKM seals under varied temperatures and compression ratios. These tests combined measurements of mass change and compression set for life prediction, along with analyses including FTIR and SEM. Results indicate that thermal oxidation exacerbates NBR aging in multiple aspects. At low temperatures, aging is dominated by cross-linking, primarily driven by the volatilization of plasticizers and antioxidants, with macroscopic property degradation proceeding slowly. At high temperatures, severe thermal oxidative aging occurs, dominated by chain scission though extensive cross-linking also takes place. This leads to rapid mass loss, increased compression set, damage to internal structure, a sharp decline in elasticity. Compression, when coupled with thermal oxidation, significantly affects elasticity changes. Both excessively low and high compression ratios reduce molecular chain flexibility, which is detrimental to maintaining elasticity. A 25% compression ratio is optimal as it slows stress relaxation and ensures better sealing reliability. Its predicted service life at 40 ℃ reaches 17.72 years, far longer than the 4.71 years at 20% and 5.53 years at 30% compression ratios. During aging, the compressed surface of the seal is less exposed to air, resulting in a lower degree of aging. FKM exhibits insignificant aging characteristics due to its superior chemical stability.

Magnetic Barkhausen Noise (MBN) is a high-frequency electromagnetic signal generated by irreversible magnetic domain wall jumps during the magnetization of ferromagnetic materials. It exhibits high sensitivity to microstructural variations, compositional changes, and stress states, making it widely applicable in non-destructive testing of ferromagnetic materials. When grain-oriented silicon steel is subjected to mechanical stress in directions deviating from its easy magnetization axis, the MBN signal undergoes alterations due to the material's anisotropic magnetization characteristics. This study investigates the variations in multiple MBN signal features of grain-oriented silicon steel sheets (30RK105) under tensile stresses of varying magnitudes and directions. Principal component analysis is employed to extract the most influential features contributing to MBN signal changes. Combined with multiple linear regression, the magnitude and direction of applied tensile stress are quantitatively evaluated. The accuracy of the proposed method is validated through calculations of root mean square error and correlation coefficient.

In traditional gas relay, heavy gas trip is judged according to the flow velocity of the pipeline, but heavy gas maloperation often occurs. In order to fully explore the action mechanism of heavy gas, a new type of gas relay that can monitor the angle of the baffle is developed, and the heavy gas signal of the gas relay and the dynamic characteristics of the baffle under the impact of transient oil flow are further studied. The flow velocity, pressure, heavy gas and the angle signal of the baffle are collected synchronously, and the correlation between the collected signals is analyzed. The test and simulation results show that under low energy excitation, heavy gas action and baffle opening depend on pipeline flow velocity. Under high energy excitation, heavy gas action and baffle opening depend on pipeline oil flow pressure. At the end of heavy gas operation and the return of baffle, the pressure decays quickly, and the return of heavy gas operation and baffle is maintained by the oil flow velocity. On this basis, a method of heavy gas action setting of gas relay is proposed, which takes into account the characteristic quantity of pipeline oil flow velocity and pressure, and can be used as the auxiliary decision basis of heavy gas trip signal of gas relay.

Formulating a reasonable unit maintenance plan is crucial for the mid/long-term stable operation of the power system. Given the smalltime resolution of the time series production simulation and the complex constraints inherent in the maintenance model, challenges arise in the direct coupling maintenance model during mid/long-term time series operation simulations of the power system. These challenges contribute to significant scale and computational complexity. To address these issues, this paper establishes a combined model that integrates mid/long-term time series operation simulation with unit maintenance for the power system. Aiming for economic optimization, the model comprehensively considers thermal power units, annual regulating hydropower units, energy storage, pumped storage, wind power and photovoltaic, along with the maintenance models for three types of units. Furthermore, this paper introduces an annual panoramic time series construction method and clustered unit combination (CUC) method to accelerate the model solution from both time and space perspectives. Finally, the effectiveness of the proposed model and the accelerated solution method is validated through numerical example analysis, demonstrating a significant improvement in solution efficiency.

With long-term operation, the gas insulation medium and pipeline materials inside the GIL may experience aging, corrosion and other problems. Regular inspection helps to detect potential fault hazards early. Compared to traditional piezoelectric ultrasonic technology, EMAT can be detected without physical contact or coupling media, with higher measurement accuracy, while showing better adaptability to changes in pipe surface roughness. However, due to the low transducer efficiency of the electromagnetic ultrasonic transducer, the echo signal is mixed with a lot of noise and interference, and the signal-to-noise ratio is low. Reasonable design of the algorithm to extract effective signals from it and improve the signal-to-noise ratio is of great significance for electromagnetic ultrasonic nondestructive testing. To solve this problem, this paper proposes an EMAT echo signal reconstruction method based on SDU-Net. First, the noisy signal and the original clean signal after data enhancement are used as the input of the deep network. Then, the network structure and loss function are optimized to improve the ability to extract features from the EMAT signal and reconstruct clean echoes. In this paper, signals with different SNR are compared with CAE and CNN methods. The results show that, compared with CAE and CNN methods, the proposed SDU-Net method can improve the SNR of reconstructed signals to more than 20 dB on average in the de-noising task, and effectively restore the echo signal.

Damp is the key step of the pollution flashover for contaminated insulators. In order to solve the problem that it is difficult to detect the wettability of contaminated insulators, this paper proposed a rapid detection method of wettability and distribution of insulator pollution layer based on spectral-image regression analysis. Firstly, the artificial fog chamber and hyperspectral imaging platform were built to prepare the different wettability contaminated insulators samples and acquire the hyperspectral images, respectively. Next, seven partial least squares regression (PLSR) models were built based on the original spectral data and six kinds of spectral preprocessing results, and the results showed that all the PLSR models had excellent effect and the determination coefficient R2 and root mean square error RMSE of spectral derivative model (RE) in validation set are 0.994 2 and 0.037 0 respectively, which can effectively improve the effect of PLSR model. Based on the RE model, three characteristic band extraction methods were further applied to remove redundant spectral data and reduce data dimension. The results showed that compared with the full-band model, three characteristic band models can effectively remove redundant spectral information and obtain better PLSR model effect. Among them, the 10 characteristic band model established by random frog method (RF) showed the best modeling performance, and the R2 and RMSE of RE-RF-PLSR model in the validation set reach 0.996 7 and 0.027 8, respectively. Finally, according to the established RE-RF-PLSR model, wettability calculation results of each pixel point can be obtained and wettability distribution visualization of pollution insulator can be realized according to the wettability value combined with pseudo-color processing.

Aiming at the problems of low detection accuracy and poor real-time performance caused by complex background and small scale targets in photovoltaic panel defect detection, a photovoltaic panel defect detection algorithm based on attention guidance and multi-scale feature fusion was proposed, called PP-YOLO. In order to enhance the feature extraction ability of irregular targets and improve the detection speed, AKConv (the Alterable Karnel Convolution) is used to replace some traditional convolution in the backbone network. The Spatial Attention and Channel Attention (SACA) mechanism is proposed in the neck network to integrate spatial attention information and channel attention information simultaneously to reduce the interference of background noise. A Self-Attention Module (SAM) embedded neck network is constructed. By capturing the global feature relationship and focusing on local semantic information, more low-level detailed features are retained, and a high-resolution detection head is added to the detection layer, so as to achieve accurate identification of small defect targets. The experimental results show that on the self-made PV panel defect detection data set, PP-YOLO's mAP reaches 95.28%, which is significantly better than the baseline model and other mainstream detection algorithms. In addition, the comparative results of embedded experiments further verify that PP-YOLO can still achieve efficient and accurate real-time detection under the environment of limited computing resources, and can provide effective technical support for real-time defect identification and automated maintenance.

With the increasing number of renewable energy inverters, the electromagnetic transient-based cumulative model for individual inverters, though accurate, introduces excessive computational redundancy, making it unsuitable for real-time requirements in multi-inverter cooperative control. Meanwhile, incomplete simplified aggregation models and conventional "one-size-fits-all" aggregation approaches fail to meet the precision requirements of power scheduling in modern power systems. To address these issues, this paper proposes a precise multi-inverter aggregation method that considers unstable operating conditions and inter-cluster interactions. First, the primary factors affecting aggregation model accuracy are analyzed based on different model types. Then, unstable operating conditions and inter-cluster interactions are incorporated into the inverter clustering determination. A weighted parameter computation approach is employed to construct an aggregation model that preserves system dynamic characteristics while ensuring power accuracy under both stable and unstable conditions. Finally, simulations involving eight single-phase grid-connected inverters under various operating conditions verify the accuracy of the proposed method.