With the rising demand for electricity driven by economic development, the safety and stability of UHVDC transmission projects—as primary power conveyance channels—have garnered widespread attention. Commutation failure, a common fault in DC transmission, poses significant transient voltage stability issues for the sending-end grid that cannot be overlooked. Establishing a mathematical model for the bus voltage at the UHVDC sending-end is the primary method for describing the voltage amplitude of the sending-end grid during commutation failure. VDCOL is frequently implemented in both sending and receiving DC converter stations to prevent damage from overcurrent during fault occurrence and recovery. However, existing mathematical models often neglect the impact of VDCOL on sending-end bus voltage throughout the entire fault process, leading to inaccuracies in analyzing the transient voltage stability of the sending-end system. To accurately analyze transient voltage characteristics at the sending end during commutation failure, this paper first describes the control structure of UHVDC systems incorporating VDCOL and addresses the modeling challenges of sending-end grid voltage in existing UHVDC systems. Subsequently, based on the segmented characteristics of DC current, a phased mathematical model of sending-end grid voltage is developed, proposing a comprehensive mathematical model for the entire transient voltage process at the sending-end grid during commutation failure faults in UHVDC systems that accounts for VDCOL. Finally, a ±800kV UHVDC transmission system simulation model was constructed in PSCAD/EMTDC to validate the accuracy of the proposed model. Results demonstrate that compared to existing mathematical models, the model established in this paper achieves higher precision and effectively captures the full-process characteristics of busbar voltages at the sending-end grid of UHVDC transmission systems incorporating VDCOL during commutation failure.

Although the current fault identification method based on deep learning performs well in terms of accuracy, the identification results mainly reflect the correlation and lack causal interpretability. Therefore, this paper proposes a fault identification method for winter transmission lines based on Bayesian Additive Regression Trees (BART), which aims to enhance the interpretability and causal inference ability of the model. First, the causes, types and corresponding weather features of transmission line faults in winter are sorted out and analyzed, and the transient waveform characteristics of typical faults including ice shedding jump, ice flashover and pollution flashover are deeply analyzed. Second, combined with causal inference principle and ensemble learning algorithm, a BART fault identification model is constructed. Using its excellent nonlinear fitting ability and built-in uncertainty quantification mechanism, the causal relationship between fault causes and meteorological factors is explored. Third, the fault type, fault cause and weather information are numerically encoded, and the three-phase current and voltage data features extracted by the residual network are used as model input to train and test the fault identification model. Finally, the experimental results based on actual fault data show that the accuracy of the proposed BART model in fault cause identification reaches 95.57%, which verifies that it has stronger interpretability and causal analysis ability while maintaining high identification accuracy.

Incipient fault detection in transmission lines is essential for early fault warning and the safe operation of power systems. However, such faults often exhibit weak waveform features that are difficult to distinguish from disturbances caused by load variations and renewable energy fluctuations. To address this issue, this study proposes a time–frequency neural network-based incipient fault recognition method with feature-level hyperparameter adaptive optimization. Unlike traditional deep learning models that rely on manually tuned hyperparameters, the proposed method takes the time–frequency neural network as the baseline architecture, treats tensor size as the key hyperparameter in the feature fusion layer, and employs a particle swarm optimization algorithm to automatically search and optimize it. This enables the adaptive determination of the optimal combination of multi-scale features, thereby significantly improving detection accuracy and system robustness. Experimental results show that the proposed method achieves superior performance compared with baseline models using manually set hyperparameters, reaching a recognition accuracy of 93.31% while significantly reducing parameter-tuning costs.

The traditional T-type three-level inverter is widely used in the field of new energy generation due to their high efficiency and low leakage current. However, their limited input voltage range fails to meet the requirements of certain renewable energy applications. In this paper, a single-phase dual T-type three-level inverter is proposed, which can adapt to a wider range of input voltage fluctuations. Three modulation methods are proposed for this inverter: high-gain three-level modulation, low-gain three-level modulation and hybrid modulation. Among them, high-gain three-level modulation is suitable for low-voltage input, which can reduce the input voltage range to 1/2 of the traditional single-phase T-type three-level inverter; low-gain three-level modulation is suitable for high-voltage input, and by controlling the two capacitors to work in high-frequency rotation, it can realize the balance of mid-point potentials and reduce the inductor current ripple. And the hybrid modulation is a combination of high-gain three-level modulation and low-gain three-level modulation, which has the advantages of wide range, high efficiency and good waveform quality. The working principle is described, the mathematical model is deduced, and a three-loop control scheme is proposed for the outer loop of output voltage, the inner loop of inductor current, and the balance loop of midpoint voltage. The experimental results of the fabricated prototype verify the circuit and its control method.

Vienna rectifiers find extensive applications in industrial and aerospace domains. Current research on open-circuit fault diagnosis and fault-tolerant control for Vienna rectifier switching devices predominantly addresses three-phase three-wire topologies, which exhibit limited noise immunity and significant output voltage fluctuations. By analyzing current path variations in fault phases, this study establishes a multi-criteria fault diagnosis mechanism. Leveraging the phase-decoupling characteristics inherent in three-phase four-wire topology, a dynamic fault-tolerant control strategy is implemented through: (i) isolation of fault phases, (ii) voltage-current phase synchronization control in healthy phases, (iii) amplitude ratio optimization, and (iv) power adaptive derating. These coordinated measures collectively suppress DC-link voltage fluctuations, reduce input current magnitude, and maintain stable system operation. Experimental validation confirms the efficacy of both the diagnostic methodology and fault-tolerant control approach, demonstrating significant improvements in transient stability and power quality preservation under fault conditions.

The concentrating solar-wind-energy storage hybrid system can fully leverage the complementary characteristics of different energy sources, thus significantly mitigating output power fluctuations and enhancing power supply quality. However, with the integration of multiple generation, the topology and control architecture of the concentrating solar-wind-energy storage hybrid system have become increasingly complex, posing challenges to its stable operation, such as multi-time-scale dynamic coupling and oscillation risks. This paper focuses on the concentrating solar-wind-energy storage hybrid system, employing time-domain and frequency-domain analysis methods to reveal its interaction mechanisms and oscillation characteristics. First, a small-signal model of the hybrid system is developed, taking into account the dynamic characteristics of concentrating solar plant, wind turbine, and energy storage system. Furthermore, based on time-domain modal analysis, the oscillation characteristics and their contributing factors are investigated, with a particular emphasis on the generation mechanism of subsynchronous oscillation. Finally, based on the modal analysis results, a supplementary damping controller is designed, and its parameters are determined using the Nyquist stability criterion to enhance the dynamic stability of the system.

High Frequency Transformers (HFT) are characterized by their compact size, lightweight, and high power density. However, they are subject to notable vibrations and acute noise during operation due to the constraints of core materials. Investigating the generation mechanisms, characteristics, and influencing factors of HFT vibrations is of significant importance. Initially, the study delves into the sources of vibration within the HFT core, exploring the mechanisms of magnetostriction and Maxwell forces. Subsequently, a simulation model for HFT vibration is developed to examine the effects of magnetostriction and Maxwell forces on core vibration under varying air gap distances. Finally, the research investigates the impact of different influencing factors on core vibration, uncovering the characteristics and patterns of vibration changes. The findings reveal that the vibration sources in transformer cores with air gaps are a combination of magnetostriction and Maxwell forces, whereas in cores without air gaps, magnetostriction predominates. The primary frequency of the transformer's vibration is twice the excitation frequency, and to a certain extent, the amplitude of vibration is positively correlated with the excitation frequency and magnetic flux density.

In response to the demand for regionalized metro power supply, this paper analyzes the formation mechanism and influencing factors of metro traction peak load. A probabilistic prediction method for traction load oriented towards an ultra-short-term time scale is proposed, which can provide effective support for ultra-short-term load forecasting and dynamic management of shared main transformers in regionalized power supply. During the analysis of the traction peak load formation mechanism, a simulation model integrating multi-train superposition and simultaneous absorption of regenerative braking energy is established. The impact of start-stop time fluctuations caused by stochastic factors on the peak load is discussed. Furthermore, kernel density estimation is employed to characterize the probability distribution of the traction load accounting for random fluctuations. This is combined with an improved Latin hypercube sampling technique to efficiently generate load random scenarios with spatial correlation. To accurately capture the ultra-short-term dynamic variation patterns of the load, Ensemble Empirical Mode Decomposition and Bidirectional Long Short-Term Memory network are adopted to decompose and predict the load sequence. Validation based on measured data from the Shanghai Metro demonstrates that this method can significantly improve the accuracy and reliability of traction load prediction, thereby providing a theoretical basis and decision support for the dynamic optimization of shared main transformer capacity and overload risk prevention and control.

Aiming at the issues of high current stress in dual active bridge (DAB) converters under single phase shift (SPS) modulation and the difficulty in accurately estimating the convergence time of super-twisting sliding mode control (STSMC), this paper proposes a current stress optimization scheme based on dual phase shift modulation and fast finite-time super-twisting sliding mode control(FFSTSMC) , building upon the conventional STSMC. Compared with STSMC, the proposed strategy exhibits stronger robustness and faster convergence, and the upper bound of its convergence time can be quantitatively estimated. Furthermore, based on this control strategy, a finite-time extended state observer is designed to estimate the load current in real time, thereby reducing the control cost of the system. In addition, Lyapunov stability theory is employed to rigorously prove the stable convergence of the designed controller and observer. Finally, simulation analysis and hardware experiments are conducted to compare the proposed scheme with traditional control strategies, verifying the effectiveness of the presented optimization approach.

Currently, hydropower units are developing towards higher single-machine capacity and higher rotational speeds, which has led to increasing difficulties in cooling the vertical motor rotors, creating an urgent demand for efficient rotor cooling technologies. This paper takes an operational hydropower unit as the research object to explore the feasibility and cooling effect of the internal pipeline evaporative cooling system. Firstly, a calculation model for rotor ventilation and heat dissipation under the existing air cooling method was established. Through fluid-solid conjugate heat transfer simulation analysis, the temperature distribution characteristics of the rotor excitation windings under air cooling were obtained, and the accuracy of the simulation method was verified by comparing with the actual measured data of the unit. Secondly, based on the air cooling configuration, a scheme of self-circulating internal pipeline evaporative cooling system for the rotor were designed. Combined with two-phase flow and heat transfer simulations, the circulation and heat transfer principle and technical feasibility of the rotor evaporative cooling system were analyzed. It was shown that the introduction of the evaporative cooling system can reduce the temperature of the excitation windings, improve the uniformity of temperature distribution, and eliminate local hot spots. The proposed cooling scheme provides feasible ideas and design references for the application of internal pipeline evaporative cooling technology in the field of vertical rotor cooling.

To solve the problems of narrow soft-switching range and complex phase-shifting strategy of the traditional single-side inductor DAB converter, this paper presents the first time domain mathematical modeling and Zero-Voltage Switching (ZVS) analysis of the split inductor DAB (SIDAB) converter, where the inductors are split on both sides of the transformer, and a simplified hybrid EPS (SHEPS) control strategy that combines the magnetizing current to achieve the full-range ZVS is proposed. This strategy can realize the full-range and full-power ZVS operation of all switches without increasing the computational complexity. In addition, to reduce the extra loss caused by the magnetizing current, this paper proposes the SHEPS control strategy to achieve the RMS optimization based on the full-range ZVS operation by utilizing the global optimal condition of the RMS current. Finally, a 3 kW/100 kHz high-frequency SIDAB prototype is built, which verifies the effectiveness of the control strategy in this paper and significantly improve the system efficiency.

The community adopts demand-side management (DSM) strategies to optimize and regulate electrical vehicles (EVs) charging. However, the uncertainty in EV charging behavior and photovoltaic (PV) output may lead to deviations in load response from expected outcomes. In addition, with the continuous increases in EV penetration, transformers face on overload risk, imposing higher requirements on the reliability of community power supply. To qualify the operational risks under different DSM strategies, this paper develops a Monte Carlo simulation-based risk assessment method for the community that includes basic loads, EVs and PV generation. Based on the relationship between load levels and transformer capacity, the community operation is classified into three states: safe, margin and risk. Two types of risk assessment indicators, probability-based and severity-based, are established to systematically evaluate the impact of DSM on community power system reliability. Finally, simulation results validate the necessity and effectiveness of the proposed risk assessment indicator system. Furthermore, we explore the impact of increasing EV penetration on community operational risk, providing guidance for enhancing the safety and reliability of community power systems.

Digital transformation is an inherent requirement for promoting the development of the smart grid. As the critical foundation for building a new-type power grid and Energy Internet, the lack of fundamental research in digital design, intelligent operation and maintenance, and knowledge sharing has become a key constraint on the digital transformation and product upgrading of power equipment. Therefore, focusing on critical stages in the full life cycle of power equipment, a technology framework for digital design and service of power equipment based on knowledge re-usage is proposed. Key technologies for the digitalization of power equipment are elaborated, including digital prototype modeling, reduced-order simulation model, and knowledge-integrated service state evaluation model. Oriented toward the Power Internet of Things, a digital model of power equipment is proposed based on the common information model (CIM) and extensible markup language (XML). The proposed model facilitates the interaction among heterogeneous systems across key stages of service tasks. It also enhances the exchange efficiency of information, such as knowledge, data, and models, thereby laying a foundation for the digital transformation of power equipment. Finally, the high-voltage circuit breaker is used as a case study to present the digitalized process of in-service diagnosis, which validates the feasibility of the proposed digital model for power equipment.

In order to solve the problems of insufficient frequency support capability, lack of frequency regulation resources and high risk of frequency instability caused by large-scale renewable energy access to the power grid, it is necessary to incorporate renewable energy stations into the existing frequency regulation control framework of power grid, and to investigate the coordination of synchronous generator units and renewable energy stations to participate in the automatic generation control (AGC) of power grid. Firstly, a frequency response model of the regional interconnected power grid containing thermal power units, hydropower units, wind power stations and photovoltaic stations with multiple types of power equipment is established. Secondly, the differences in the frequency regulation response characteristics of thermal power unit, hydropower unit and renewable energy station are analyzed. Considering the differences in the frequency regulation response characteristics of renewable energy station and conventional synchronous generator unit, the calculation method of the AGC power allocation factor in the process of frequency change of regional power grid is proposed. On this basis, the AGC strategy with dynamic updating of the power allocation factor is proposed. Finally, the frequency response model of the two-area interconnected power grid is established on Matlab/Simulink, and simulations are carried out to verify the correctness and effectiveness of the proposed strategy. The simulation results show that, compared with the traditional AGC power allocation method, the proposed strategy can effectively improve the frequency response index and control performance standards of power grid, and enhance the frequency stability of regional interconnected power grid.

The metallic return arrester (EM arrester) is a critical protective device in ultra-high-voltage direct current (UHVDC) converter stations, serving to suppress operating overvoltages and absorb surge energy. Its electro-thermal stability directly determines the reliability of system operation. Addressing the insufficient attention in existing research to the thermal behavior and energy withstand capability of UHV EM arrester, this study establishes a sequentially coupled simulation framework that integrates a three-dimensional finite element model with an electromagnetic transient equivalent circuit. The proposed method enables a systematic investigation of the voltage-current response, power dissipation distribution, and temperature rise evolution of EM arrester under operating overvoltage conditions. The results reveal that the overall temperature rise remains relatively low, with the maximum temperature concentrated in the central region of the resistor blocks, while both ends exhibit lower temperatures due to enhanced heat dissipation through conductors. Furthermore, current non-uniformity has only a marginal influence on the global thermal field distribution under typical conditions. The accuracy of the model is validated by resistor block temperature rise and energy withstand tests, with discrepancies between simulation and measurement within 8%. These findings demonstrate that the proposed model provides an effective tool for evaluating the energy absorption and thermal performance of EM arrester, thereby offering theoretical support for design optimization and operational risk assessment in UHVDC systems.

Dual active bridge (DAB) DC-DC converters play a key role in bidirectional power transfer. To improve their operating efficiency under input-output voltage mismatch conditions while ensuring zero-voltage switching (ZVS) for power switches, we need to optimize the converter's reactive power. For this purpose, this paper proposes a new ZVS-based extended phase-shift control strategy to achieve minimum reactive power over the full power range.First, we redefine the phase shift ratio to classify operating modes, establish a reactive power model, and select the optimal operating mode. Then, by integrating ZVS constraints, we obtain ZVS-based minimum reactive power control for the first segment of the transmitted power range. For the second segment of the transmitted power range, we optimize the reactive power by introducing the derivative method and the Karush-Kuhn-Tucker (KKT) condition method, and then conduct comparative analysis on their optimization results: when the converter operates in high-power mode, the two schemes achieve consistent optimization effects; when the converter operates in low-power mode, the KKT condition method shows more significant optimization effects on reactive power and current stress. Finally, experimental verification shows that the proposed strategy can effectively improve the converter's performance over the full power range.

The AC arc furnace based on the flexible power supply and distribution scheme (hereinafter referred to as "flexible AC arc furnace") is a new type of arc furnace technology, featuring high system control accuracy and fast response speed. It can achieve stepless regulation of arc power, which is of great significance for reducing the energy consumption of arc furnace smelting. However, at present, there is no unified and effective method for analyzing the electrical characteristics of this type of arc furnace load, and there is a lack of analysis of its power supply mode and definition of the operating boundaries. Therefore, this paper takes the output voltage and current of the converter of the flexible AC arc furnace as independent variables and power as dependent variables to construct the load electrical operation diagram. With the rated capacity constraint of the converter and the preset power factor constraint as conditions, the power circle diagram of the arc furnace is established. Based on this, the operating conditions and stable operation regions corresponding to different outputs under two control modes of the flexible AC arc furnace are obtained, and the influence laws of different arc lengths on power distribution and smelting efficiency are revealed. Through the analysis of the power demand of the arc furnace operating point, it is found that the current control mode can effectively avoid the risk of operating point drift and significantly improve the accuracy and reliability of system control. The above results provide a theoretical basis for the formulation of the arc furnace power supply curve and the selection of power supply strategies.

Grid-forming control supports the “dual-carbon” goals and the evolution of power systems with high renewables and high power electronics. It enables renewable energy systems to provide autonomous inertia response. Inertial synchronization control is a type of grid-forming control. It has a simple structure and is often used in grid-side converters of renewable generation systems. However, in doubly-fed induction generator (DFIG) based wind power systems, the main path for inertia response does not go through the grid-side converter. Existing schemes only use the grid-forming capability of the rotor-side converter to provide inertia. This leaves room for improving inertia transmission in grid-forming DFIG wind turbines. The mapping between DC-link voltage and grid frequency under inertial synchronization control offers a practical way to enhance inertia response. This paper proposes a disturb-ance-adaptive active power compensation strategy. It is driven by grid-side inertial synchronization control. This strategy allows both the grid-side and rotor-side converters to jointly support inertia response. It enables fast active power adjustment during frequency disturbances. Additionally, inner-loop current control is added to the inertial synchronization structure. This improves current regulation accuracy and enhances the dynamic response of the DC-link voltage under disturbances. Simulation results show that the proposed strategy provides faster inertia release, better frequency recovery, and higher system dynamic stability throughout frequency events. The method effectively improves the inertia response capability of DFIG wind turbines and enhances system stability.

To address the technical bottleneck of balancing power allocation accuracy and dynamic response speed in Vehicle-to-Grid integrated multi-port energy routers, a hierarchical cooperative architecture featuring "Energy Management-Coordinated Control-Fast Execution" is proposed. Specifically, the upper layer determines system-level energy scheduling and mode switching for global optimization; the middle layer constructs an adaptive voltage segmentation mechanism integrating battery State of Charge to achieve multi-objective coordination; and the bottom layer introduces bus voltage differential feedforward compensation to guarantee fast and precise instruction execution. Simulation results demonstrate that the proposed scheme effectively overcomes the inherent limitations of existing methods. Compared with typical conventional control, the recovery time under strong disturbances is shortened by 90%. In islanded mode, the EV power tracking error under heavy loads is restricted to within 1%; in grid-connected mode, adaptive cooperative support for grid power is realized, and DC bus voltage fluctuations are restricted to within 2% under all operating conditions. Consequently, the strong coupling constraints among multiple objectives are effectively decoupled, significantly enhancing system dynamic robustness and power quality.

Autonomous electric vehicles represent a pivotal convergence of global energy transition and smart transportation. Their large-scale deployment is reshaping vehicles, transport networks, and power grids into deeply integrated systems. This paper develops an optimized charging scheduling method for mass AEV deployment. The approach establishes a vehicle-transport-grid coupling model to enhance scheduling efficiency and prevent local overloads. We analyze spatiotemporal load distributions to extract key factors including charging pile occupancy rates and dynamic electricity prices. Using improved K-means++ clustering, we group charging nodes to reduce optimization complexity. The system then recommends optimal charging nodes to users based on travel distance and duration. This methodology enables orderly charging while maintaining system balance. Results demonstrate our strategy significantly improves optimization efficiency, balances load distribution, and reduces user charging costs, confirming its practical effectiveness.

As the core equipment for renewable energy grid connection, the synchronization stability of the grid-following (GFL) converter is affected by the control loops coupling and the grid strength, which will have a negative impact on the grid security. In order to improve the synchronization stability of the GFL converter under weak grid, the phase-looked loop (PLL) model is established to consider the effects of control loops coupling in this paper. By analogy with the synchronous generator rotor dynamics equations, the PLL model is transformed into the Heffron-Phillips model, and the influence of damping and synchronization components on the synchronous stability of the GFL converter is analyzed by using the complex torque analysis method. Then, by analyzing the influence of control parameters and line parameters on the damping component, the influence of control coupling and power grid strength on the synchronous stability of the GFL converter is revealed, and the design principles of the control parameters are given. Finally, a phase compensation strategy for equivalent input torque is proposed to improve the synchronous stability of the GFL converter by weakening the influence of the control coupling on the damping component. Simulation and experimental results verify the correctness of the GFL converter model analysis and the effectiveness of the phase compensation strategy.

This paper addresses the issue of increased healthy pole voltage in a radial modular multilevel medium voltage direct current (MMC-MVDC) distribution network with small current grounding when a single-pole grounding fault occurs. A fault detection method based on data normalization is proposed. By analyzing the transient current loop and the characteristics of pole current fault component under different grounding methods, the limitations of directly using pole current fault component for fault detection are pointed out. This method utilizes max-min normalization technology to render the quantities dimensionless. By doing so, it effectively amplifies the differences in fault characteristics while preserving the waveform morphological features, thereby establishing the criteria for fault line and pole selection. PSCAD simulation verification demonstrates that this method can accurately identify line and busbar faults using only a 1ms data window. It not only exhibits a strong ability to withstand high transition resistance but also adapts well to complex conditions such as load fluctuations, extremely short lines, and low sampling rates, indicating significant value for engineering applications.

Wind turbines typically provide transient frequency support to power systems through droop control and virtual inertia control. If the frequency regulation coefficients are too small, the support provided is insufficient. Conversely, excessively large coefficients may compromise system stability. Therefore, it is essential to characterize the allowable operating region of these coefficients. This paper first establishes a mathematical model of the grid-connected Permanent Magnet Synchronous Generator (PMSG)-based wind turbine system. Analytical relationships among the droop coefficient, power disturbance, and steady-state rotor speed are derived, followed by the definition of a permissible range for the droop coefficient that ensures the existence of an equilibrium point. Subsequently, based on the Routh-Hurwitz criterion, necessary and sufficient conditions for small-signal stability of the grid-connected wind turbine system are derived. It is found that when the steady-state rotor speed is low, the system only exhibits unstable equilibrium points, which leads to the derivation of analytical expressions for the critical steady-state rotor speed and the critical droop coefficient. By applying algebraic criteria for small-signal stability, a mathematical relationship between the droop coefficient and the critical inertia coefficient is further established. This provides a method to characterize the operational region of the wind turbine's frequency regulation coefficients. Case studies are conducted to validate the accuracy of the proposed method for determining this operational region.

To assess the noise in flexible HVDC converter stations, the vibration characteristics of metallized film capacitors adopted in the converter station was tested. First, a bipolar plate model incorporating space charges is proposed to explain the phenomenon of metallized film capacitors generating vibration components at the same frequency as the voltage under single-frequency AC voltage excitation. Subsequently, the vibration characteristics of metallized film capacitors under AC and AC-DC superimposed conditions are investigated using a vibration test platform, validating the effectiveness of the model. Finally, the conditions for generating vibrations at the same frequency as the AC voltage in metallized film capacitors are studied. The results indicate that the bipolar plate model with space charges effectively reflects the vibration characteristics of metallized film capacitors. The magnitude of the internal space charges in the model is related to the DC pre-stress applied to the metallized film capacitor, which influences the magnitude of the vibration acceleration at the same frequency as the voltage. This study provides a basis for understanding the mechanism behind vibration generation in metallized film capacitors.

Accurate SOC estimation is essential for ensuring the safe operation of lithium-ion batteries. To address the limitations of existing deep learning methods, such as insufficient input features and degraded performance under complex operating conditions, this paper proposes a SOC estimation approach that integrates a physical battery model with deep learning algorithms. First, the first-order RC model is employed for parameter identification, extracting ohmic resistance, polarization resistance, and polarization capacitance as physics-informed features, which are combined with measured voltage, current, and temperature to enhance representation under complex conditions. Then, TCN captures local temporal patterns, while SENet adjusts channel-wise feature weights to obtain an initial SOC estimate. The initial estimate is fused with original features and fed into an Informer model to learn dynamic characteristics and long-term dependencies for final SOC prediction. Validation on public battery datasets demonstrates that the proposed method achieves a maximum MAE of 0.56% and RMSE of 0.64% under various temperatures and operating conditions, confirming its effectiveness.

Aiming at the problem of large fluctuation of wind and solar new energy access to the power grid, this paper studies the power allocation strategy of electro-hydrogen energy storage system based on multi-type electrolytic cell to stabilize the fluctuation of wind and solar, and the key role of this strategy in capacity optimization configuration. First of all, the architecture of electro-hydrogen energy storage system is established. In order to improve the effect of existing decomposition methods, a wind-solar power decomposition method based on time-of-use electricity price under multi-granularity mechanism is proposed. Then, the working characteristics of two kinds of electrolytic cells are analyzed, and the power distribution strategy of high proportion hydrogen storage considering multi-electrolysis type is proposed, and the capacity optimization configuration model of hybrid energy storage system with the goal of smoothing wind and solar fluctuations is established. Finally, through the example analysis, compared with the power allocation strategy of a single hydrogen storage system, the capacity allocation scheme of the proposed strategy in this paper reduces the capacity and power of electrochemical energy storage while improving the stabilization effect, effectively improves the operation economy of the energy storage system, and plays the main consumption compensation role of hydrogen energy storage.

Current research on gravity energy storage systems (GESS) typically adopts rigid or low-dimensional simplified models for transmission mechanisms such as wire ropes, without fully accounting for their flexibility and the resulting high-dimensional nonlinear dynamic behavior. Although existing studies in the mining and elevator fields do address mechanical vibration, they mainly focus on mechanical-side effects such as impact loads and guide-structure responses, and research on how mechanical vibrations, through electromechanical coupling mechanisms, in turn affect the dynamic performance of electrical systems remains insufficient. Against this background, this paper adopts an electromechanical-coupling perspective and aims to accurately characterize the system’s dynamic operating behavior by elucidating the coupling mechanism between mechanical vibrations and oscillations in electrical quantities. A time-domain model for the longitudinal vibration of the wire rope is developed based on the Rayleigh method, and the dynamic load characteristics are incorporated into the motor motion equations. The influence of mechanical vibrations on the grid-side current, after being modulated by the motor and dual PWM converters, is analyzed and further examined through simulations and experiments. The results show that the low-frequency vibration of the elastic wire rope generates characteristic sidebands and beat frequencies on the grid side via electromechanical coupling, thereby inducing power oscillations. Under deep-well operating conditions, the time-varying stiffness caused by rope-length variation introduces interharmonics into the grid-connected current, which markedly affects system stability. This work elucidates the electromechanical coupling mechanisms in GESS and discusses the grid-connected oscillation characteristics induced by mechanical-side vibrations, thus providing a solid theoretical basis for optimal system design, stability analysis, and power-smoothing strategies.

Argon–mercury (Ar-Hg) gas discharge is widely used in high-efficiency lighting and ultraviolet sterilization, yet its complex microscopic physical processes and discharge mechanisms remain incompletely understood. In this study, a plasma fluid model of the discharge tube is coupled with an external circuit. The discharge tube is modeled as a one-dimensional axial Ar–Hg plasma discharge driven by an AC power source. A self-consistent solution is obtained for the external circuit behavior and the axial distributions of electron density, electron temperature, and collision frequency within the tube. The results indicate that during the initial discharge stage, the tube voltage continuously decreases while the discharge current increases slightly. After reaching steady state, particle densities are higher near both electrodes and become nearly uniform along the axial direction. Under a ballast resistance of 400 Ω, the electron density reaches 1.9×1018 m-3. A maximum electron density of 4.7×1018 m-3 is achieved at an argon partial pressure of 57 torr. With increasing argon partial pressure, the electron temperature decreases from 0.71 eV to 0.36 eV, whereas the collision frequency increases from 0.5 GHz to 11.8 GHz. These findings provide a theoretical basis for optimizing electron density in Ar–Hg gas discharges.

This paper investigates the issue of floating potential arising on the receiver-side electrical shielding layer during the operation of wireless power transfer (WPT) systems. Through multiple experimental observations, contact spark discharge was identified at the tips of the shielding layer, suggesting the likely occurrence of corona discharge and its potential influence on the magnitude of the floating potential. To verify this phenomenon, the existence of corona discharge at the shielding layer tips was experimentally confirmed. For the analysis of floating potential formation, an equivalent circuit model of the system was established, and a theoretical calculation formula for floating potential that accounts for capacitive effects was derived. Based on the principles of electromagnetic induction, capacitive coupling, and electrostatic field effects, the induced voltage, capacitive voltage, and discharge voltage on the electric shielding layer were calculated separately. This work systematically reveals the composition of the floating potential on the electric shielding layer and its discharge characteristics.

The safety distance between inspection drones and power transmission lines is a key factor in ensuring safe operation and maintenance. Currently, the determination of safety distances relies primarily on optical methods, with little consideration given to the influence of magnetic fields; however, the strong magnetic fields generated by high-voltage transmission lines may interfere with the drone’s onboard sensors. This paper focuses on the development of a magnetic field safety threshold alarm device for establishing safety distances. First, finite element simulation was used to analyze the attenuation patterns and spatial distribution characteristics of the magnetic field around a ±500 kV double-circuit HVDC transmission line on a single tower, thereby preliminarily determining the magnetic field detection range and early warning thresholds. Subsequently, based on the estimated parameter ranges, a magnetic field safety threshold alarm device was designed and developed to provide reliable, field-measured data support for establishing magnetic field safety distances. Finally, through laboratory and field tests conducted at a ±500 kV HVDC transmission line site, the accuracy of the device in detection and alarm functions was verified. By combining simulation with field measurements, this study provides theoretical references and technical grounds for establishing safety distances for UAV inspection operations in the magnetic field environment of HVDC transmission lines, thereby helping to enhance the safety and reliability of inspection operations in complex electromagnetic environments.

For linear motors, the broken magnetic circuit due to the end effect leads to asymmetry in electromagnetic parameters, which adversely affects the speed identification accuracy of sensorless control systems based on the model reference adaptive system (MRAS). This paper investigates a 12/13 slot-pole linear flux-switching permanent magnet motor (LFSPM). First, a precise mathematical model considering asymmetric electromagnetic parameters is established. Second, the mechanism by which electromagnetic parameter asymmetry in the LFSPM impacts MRAS-based speed identification is revealed. Then, an improved MRAS speed identification method that accounts for the electromagnetic parameter asymmetry is proposed, and the complete control system is developed. Finally, simulation and experimental results demonstrate that, compared with the conventional MRAS method, the proposed improved method significantly suppresses the speed and position estimation errors caused by electromagnetic parameter asymmetry, achieving higher precision in sensorless control. This work provides an effective solution for high-precision sensorless control of linear motors.

This paper focuses on the dynamic evolution of oil film stiffness of plain bearings of primary fans of thermal power units under deep peak shaving conditions and its influence mechanism on vibration. Breaking through the traditional static analysis, a three-dimensional bearing model with fluid-structure coupling is innovatively constructed, and the nonlinear oil film force is mapped to the dynamic stiffness and damping parameters that change with the load in real time with the load. It is found that the sudden drop in load leads to the reduction of the radial force of the bearing, which induces the nonlinear attenuation of the oil film support stiffness, significantly reduces the stability of the system and amplifies the vibration amplitude, revealing the mechanism of load change by affecting the stiffness of the oil film and then intensifying the vibration. The research results are verified by the measured vibration data of the power plant, which provides a theoretical basis for the real-time evaluation of bearing stability in the peak shaving process of the unit.

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.