To address the low output voltage of renewable energy systems and the resulting demand for high step-up inversion, a novel single-source switched-capacitor multilevel inverter topology is proposed. By employing the stepwise charging of switched-capacitor cells, the proposed topology achieves a voltage gain of nearly 10 from a single DC source. In conjunction with a phase-disposition pulse-width modulation (PD-PWM) strategy, it generates a 21-level AC output voltage while requiring relatively few components. Moreover, the inverter achieves self-balancing of the capacitor voltages without an additional voltage-balancing control scheme, thereby reducing control complexity. The operating principle, modulation strategy, and capacitor parameter design of the proposed topology are analyzed, and comparisons with existing single-source multilevel inverters are presented. A simulation model and an experimental prototype of the proposed 21-level high-gain inverter are developed for verification. The results demonstrate that the proposed topology requires fewer power switches and capacitors while providing a higher voltage-boosting capability for the same number of output voltage levels. Under variations in the modulation index and load conditions, the inverter output voltage remains stable. The measured voltage fluctuations across the capacitors further confirm that satisfactory capacitor voltage balancing can be achieved using the proposed modulation strategy without an additional balancing control circuit. Therefore, the proposed inverter topology is suitable for high-gain DC–AC conversion applications in low-voltage renewable energy systems, such as photovoltaic generation systems.

To address the phenomena of seasonality, inter-annual periodicity, and attention dispersion in transmission line icing thickness forecasting, this paper proposes a Gated Recurrent Unit (GRU) forecasting model based on a stage-aware dual attention mechanism and Kalman filtering. First, a temporal feature mapping is constructed to accurately characterize the seasonal and inter-annual periodic patterns of icing. Simultaneously, Kalman filtering is employed for noise suppression on the observation data to further improve prediction accuracy. Subsequently, a stage-aware feature attention and a temporal attention are introduced into the GRU network, effectively resolving the problems of insufficient identification of feature importance in different icing stages and the inadequate utilization of key information from historical sequences in traditional methods. Finally, the proposed model is validated using measured data from a 110 kV transmission line in Guizhou. The results show that the model achieves optimal performance across all evaluation metrics, with a Root Mean Square Error (RMSE) of 1.815 6 and a Coefficient of Determination (R²) of 0.909 9. Furthermore, the stage-aware attention mechanism can further improve the prediction accuracy by 10.01%, verifying the effectiveness and superiority of the proposed method.

Driven by the "double carbon" goal and the construction of new power system, the distribution network is transforming into an interactive active distribution network integrating" source-network-load-storage. " The operation reliability of energy storage system under high frequency dynamic conditions has become a technical bottleneck restricting its long-term operation. To this end, this paper proposes a reliability evaluation strategy of energy storage converter for distribution network optimization task profile. Firstly, the power four-quadrant operation characteristics of energy storage converter in participating in reactive power and voltage optimization regulation of distribution network are analyzed, and the optimal operation model of distribution network considering network loss optimization and security constraints is constructed. The key factors affecting the life and reliability of energy storage converter are analyzed. Secondly, aiming at the problem of low computational efficiency of traditional electro-thermal coupling model, a fast calculation method of junction temperature of power devices based on double-layer XGBoost structure is proposed. The high-precision online mapping of IGBT junction temperature is realized by ' main prediction layer ' and 'error correction layer'. Then, the thermal load information in the irregular junction temperature waveform is extracted by the rain flow counting method. Combined with the Miner linear cumulative damage criterion and Monte-Carlo simulation, a quantitative evaluation model for the life distribution and reliability of power devices considering parameter uncertainty is established. Finally, the IEEE 33-bus distribution system and the IEEE 118-bus distribution system are used to verify the proposed strategy. The results show that the proposed strategy can accurately capture the dynamic electro-thermal stress cycle caused by the optimization task profile, and effectively quantify the health state evolution and failure probability of the energy storage converter in the grid operation scenario.

Aiming at the poor control effect of model predictive control strategy for LCL grid-connected inverter in case of parameter mismatch and the distortion of single vector model predictive control, a hybrid vector model-free predictive control based on adaptive super-twisting sliding mode observer (ASTSMO-MFPC-HV) is proposed. Firstly, a hyperlocal model of the LCL grid-connected inverter is established, and a hyper-helical sliding mode observer is introduced to estimate the nonlinear part. Secondly, the tracking error is detected by designing adaptive variables and the sliding mode gain is adjusted dynamically. Finally, the corresponding inverter-side voltage vector is inferred through the prediction equation of each control target and its sector position is determined, and then the hybrid vector combination is traversed to find the optimal solution. The experimental results show that compared with single-vector, dual-vector and triple-vector control, the proposed hybrid vector control has better steady-state effect; compared with STSMO-MFPC-HV, the dynamic and steady-state performance of ASTSMO-MFPC-HV are significantly improved.

To address the voltage violation issues caused by the integration of electrified railways into a weak power grid in a western region of China, this paper proposes a multi-scenario distributionally robust optimal dispatch method for weak power grids with traction loads. First, a system flexibility supply model incorporating energy storage stations and transferable loads is established. Second, a Wasserstein-distance-based ambiguity set is constructed to characterize the dual uncertainties associated with photovoltaic (PV) generation and multi-train traction loads. Latin hypercube sampling is employed to generate representative scenarios of PV output and three-phase loads. For traction loads, kernel density estimation (KDE) combined with a joint Copula function is utilized to model the uncertainty of individual train loads, and representative load scenarios for each traction substation are generated based on multi-train timetables. The ambiguity set constructed from the representative source–load scenarios is then incorporated into a two-stage distributionally robust optimal dispatch framework. An improved column-and-constraint generation (C&CG) algorithm is adopted to achieve efficient solution of the proposed model. Finally, simulations are conducted on an 8-bus weak power grid system in a western region of China. The results demonstrate that the proposed method can effectively mitigate voltage violations caused by traction loads and enhance both the operational economy and security of weak power grids under dual source–load uncertainties.

With the increasing integration of high shares of renewable energy sources and power electronic devices, modern power systems exhibit strongly coupled multi-time-scale dynamic characteristics. Conventional electromagnetic transient (EMT) simulation methods with uniform fixed time-step integration suffer from a significant trade-off between computational accuracy and efficiency. To address this issue, this paper proposes an adaptive step-size based multi-rate electromagnetic transient parallel simulation method.First, a multi-subsystem multi-rate parallel simulation framework is developed to represent multi-time-scale dynamic behaviors, enabling coordinated time-step integration and synchronization between fast and slow subsystems with heterogeneous dynamic characteristics. Second, an adaptive time-step control strategy based on local truncation error estimation is introduced, allowing real-time adjustment of simulation step sizes according to system dynamic variations. Furthermore, an improved interface data update and synchronization mechanism is designed to reduce the impact of interface errors on numerical accuracy and stability during multi-rate coupling. Finally, the proposed method is validated through representative case studies. Simulation results demonstrate that the proposed approach significantly improves computational efficiency while maintaining high accuracy, and exhibits strong numerical stability under severe disturbance conditions, indicating its potential for practical engineering applications.

The large-scale integration of renewable energy and the widespread deployment of high-power electronic equipment in modern power grids have imposed stringent requirements on the detection of complex residual currents. Since distinct categories of residual currents necessitate specific protection strategies, identification accuracy directly dictates the reliability of protective devices. To address this challenge, this paper proposes a highly efficient method for residual current waveform identification based on an improved random forest algorithm. Specifically, eight computationally lightweight features are extracted for real-time analysis, enabling the accurate classification of all "Type A + 6" residual current waveforms specified by the latest national standards. Evaluated using measured data from a standard test platform injected with 0–10% random noise, the proposed method achieves an exceptional accuracy of 99.77%, demonstrating robust anti-interference capabilities and high suitability for practical engineering environments. Compared to existing literature, this approach successfully identifies all waveform types utilizing reduced feature dimensionality, a highly extensible feature set, and minimal computational overhead. By maintaining superior recognition accuracy while ensuring compatibility with embedded systems, the proposed method exhibits significant potential for engineering applications.

This paper proposes a stage-aware reinforcement-learning-based multi-time-scale self-tuning method for the virtual synchronous generator (VSG) parameters of grid-forming converters. The method employs a single-actor, dual-head soft actor–critic (SAC) architecture, in which stage weights are incorporated into the state vector and used to gate the action outputs. J and D are updated at a base period of 20 ms, whereas Ki is updated at a slow period of 0.5 s and held constant between successive slow updates. Action-rate limiting and parameter projection are applied to suppress abrupt parameter variations. In ablation tests with identical stage inputs and actor-network sizes, output gating reduces the maximum frequency deviation from 0.1231 Hz to 0.0891 Hz, while removing the smoothing mechanism increases the total variation of the actions from 0.9056 pu to 1.1305 pu Under an unseen equal-magnitude load decrease and a 1.5-fold load increase, the frozen policy remains within the prescribed parameter bounds and satisfies the action-rate limits in the considered single grid-forming converter grid-connected scenario.

Interturn short circuit of rotor winding is a common electrical fault in synchronous generators, especially in large units. This paper first uses the air-gap magnetic flux method to analyze the harmonic characteristics of the main magnetic field after a rotor winding inter-turn short circuit fault. Based on the correlation between the leakage magnetic field and the main magnetic field, it further predicts the fault characteristic frequencies of the leakage magnetic field.. Subsequently, taking a fault simulation synchronous generator as an example, different degrees of inter-turn short circuit faults were manufactured. The three‑dimensional external leakage magnetic field distributions are measured experimentally in both the straight‑section region and the end‑winding region. The data showed that the radial component of the leakage magnetic field near the generator's straight section could reflect the rotor winding inter-turn short circuit fault, while the tangential and axial magnetic fields were not suitable for detecting the fault. The leakage magnetic field at the end of the generator could not be used as a reliable criterion for rotor winding inter-turn short circuit fault.

Electromagnetic compatibility (EMC) pre-layout design for electric Vertical Take-Off and Landing (eVTOL) aircraft typically requires repetitive simulation evaluations across multiple candidate locations, which entails high computational costs and prolonged research and development cycles. Current full-wave electromagnetic simulation methods involve substantial time overhead, while simplified modeling methods often lack universality, making them unsuitable for rapid multi-position analysis on complex platforms. The electromagnetic field reciprocity theorem can transform the computation of receiving responses in complex models into reciprocal process problems, thereby enhancing computational efficiency. Based on the electromagnetic field reciprocity theorem, this paper proposes an efficient electromagnetic interference (EMI) calculation method. Grounded in the Huygens equivalence principle, the Lorentz direct port method and the Feld-Tai generalized response method are derived within an equivalent reciprocity process. Using the external antenna port voltage as the target metric, calculations were conducted for multiple candidate positions at 400 MHz, 800 MHz, and 1.2 GHz, and validated against full-wave simulation results. The results demonstrate that the original calculation results of both reciprocity methods can reflect the variation trends of port responses across different layouts, although deviations from full-wave results remain. Following calibration, the relative error of the S21 parameter for the Lorentz method is within ±0.06%, while the Feld-Tai method remains within ±1.5% for the cases presented. As the number of pre-layout iterations increases from 2 to 11, the total full-wave computation time ranges from approximately 1.07 to 1.49 times that of the reciprocity method, indicating that the efficiency gains of the proposed approach become more pronounced as the number of trials grows. The experimental tests and simulations have good consistency results. The proposed method provides an efficient analysis framework for external antenna layout optimization and EMC pre-layout design in eVTOL platforms, offering high accuracy and efficiency while effectively reducing the time cost of repetitive multi-position analysis and accelerating the overall development process.

To address the problems of insufficient local short-circuit ratio and limited photovoltaic delivery capability after the centralized integration of distributed photovoltaics into distribution networks, an RPFC-based method for improving the short-circuit ratio of weak distribution networks is proposed. First, according to the steady-state operating mechanism of RPFC after being connected in series with the line, an equivalent controllable complex impedance model of RPFC is established, and its feasible region is described by an impedance circle. Then, an MSCR calculation model is established based on the Zbus nodal impedance matrix, in which nodal self-impedance, mutual impedance, and photovoltaic capacity are incorporated into the short-circuit ratio constraints. Furthermore, a bi-level solution framework combining IHPSO and SOCP is constructed. The outer-layer algorithm searches for the installation branch and rated capacity of RPFC, while the inner-layer SOCP solves the optimal operating point under a fixed scheme. The proposed method is verified using a modified IEEE 33-bus system. The results show that the equivalent photovoltaic curtailment rate of the system is reduced from approximately 62.0% to nearly zero. Algorithm comparison results indicate that the proposed algorithm can improve the solution efficiency of the RPFC siting and sizing problem while maintaining consistency between the short-circuit ratio constraints and RPFC operating constraints.

Due to long-term exposure to thermal stress induced by ripple current, the gradual degradation of the capacitance and equivalent series resistance (ESR) of the DC-link capacitor has become a critical factor affecting the long-term reliable operation of three-phase grid-connected converters. However, traditional parameter identification methods usually rely on additional excitation signal injection and offline testing, or require ultra-high-frequency sampling to capture characteristic information, which makes it difficult to meet the requirements of convenient online implementation and limited hardware resources. This paper proposes an online parameter identification method for DC-link capacitors based on Physics-Embedded Machine Learning (PEML). By embedding the dynamic model of the three-phase grid-connected converter into the loss function of the neural network, the proposed method enables the network to automatically converge to physically consistent estimated capacitor parameters while fitting the observable electrical quantities. The method only requires low-frequency sampling data synchronized with PWM switching, which can be directly adapted to the existing sampling architecture of microcontrollers without introducing additional ultra-high-frequency sampling hardware. Meanwhile, the proposed method eliminates the need for injecting extra excitation signals; it can accurately identify the capacitance and ESR of the DC-link capacitor using only a small number of sampling points under natural operating conditions. It provides a low-hardware-threshold and data-efficient solution for online aging monitoring of DC-link capacitors in three-phase grid-connected converters.

The grid integration of high-penetration renewable energy sources and power-electronic equipment may increase the decay time constant of the DC component in short-circuit currents, thereby increasing the severity of asymmetric interruption for SF₆ circuit breakers. The arcing interval is defined as the range of arcing time within which a circuit breaker can successfully interrupt current under a specified operating condition. However, the influence of the DC component time constant on the boundaries of this interval still requires quantitative investigation. In this paper, a 252 kV/63 kA double-motion self-blast SF₆ circuit breaker is investigated. T100a synthetic tests are carried out under DC component time constants of 75 ms and 120 ms. An arc enthalpy-flow model is established, and an MHD-CFD model is used for cross-comparison of the current-zero pressure and current-zero temperature in the thermal expansion chamber. The results show that, when the time constant increases from 45 ms to 150 ms, the current-zero pressure in the thermal expansion chamber under the 18.9 ms arcing condition increases from 2.82 MPa to 3.81 MPa, while the current-zero temperature increases from 1 063 K to 1 281 K. Compared with the standard 75 ms condition, the predicted arcing interval under the 150 ms condition changes from 10.9~18.9 ms to 9.6~15.5 ms, and its length decreases from 8.0 ms to 5.9 ms, corresponding to a reduction of 26.25%. The results provide a reference for evaluating the arcing interval in T100a tests under high-DC-component conditions.

Aiming at the problems of low diagnosis accuracy and insufficient robustness in drive fault diagnosis of PWM hydrogen production power supply under multiple working conditions, this paper proposes a drive fault diagnosis method based on dual-branch network. Firstly, the three-phase input current of PWM hydrogen production power supply is collected as the original signal. Successive variational mode decomposition (SVMD) is adopted to obtain intrinsic mode functions (IMF) in different frequency bands, and the IMF containing main fault features is selected for signal reconstruction. One-dimensional combination and normalization are performed on the reconstructed signals to obtain standardized samples. Secondly, the fixed-radius nearest neighbor (FRNN) algorithm is used to construct graph structures from standardized samples, which are imported into graph convolutional network (GCN) to extract spatial node features. Meanwhile, the standardized samples are fed into bidirectional gated recurrent unit (BiGRU) optimized by global attention mechanism (GAM) to realize temporal feature extraction. Finally, the spatial and temporal features are fused and sent into Softmax classifier to complete fault diagnosis. Simulation results show that the proposed method achieves 100% diagnosis accuracy for 22 operating conditions, and maintains high diagnosis accuracy and strong robustness under various complex working conditions, which possesses good engineering application prospect.

PV power output is characterized by significant randomness, fluctuation, and intermittency, and its uncertainty may affect power system planning assessment, dispatch decision-making, and real-time operation. To systematically understand the development status of uncertainty modeling methods for PV power generation, this paper reviews year-round PV scenario generation and multi-timescale power forecasting, and analyzes the relationships among PV output, influencing factors, and system applications from three dimensions: statistical uncertainty, variable relationship modeling, and artificial intelligence methods. For the year-round scale, meteorology-driven scenario generation methods for long time series are discussed, including probabilistic statistical methods, deep generative methods, relational constraints, and engineering-oriented evaluation issues. For the day-ahead scale, feature extraction, probabilistic forecasting, and robust modeling methods driven by historical power, meteorological variables, and numerical weather prediction are reviewed. For the intraday scale, signal decomposition, deep learning, regional collaborative modeling, and multi-source perception methods for short-term power fluctuation awareness are summarized. On this basis, future research directions, including multi-timescale collaborative modeling, multi-source heterogeneous information fusion, controllable generative modeling, extreme scenario characterization, and engineering closed-loop validation, are further analyzed. This paper provides a reference for scenario generation, power forecasting, and power system operation analysis under high PV penetration.

This paper addresses the high-loss and multi-source coupling temperature rise problem of high-speed permanent magnet synchronous motors in aviation.A new composite cooling structure that synchronously cools the windings,the core part of the stator and the tooth part of the stator and is a global immersion type is proposed.Using the fluid-solid coupling simulation method,the influence laws of the number of oil channels in the yoke,the cross-sectional size of the oil channels,the radical position and diameter of the oil channels in the slot on the temperature rise of the motor are systematically studied.This structure is compared with the cooling scheme of the tooth part and the yoke+tooth part.The result shows that when the ratio of the number of core oil channels to the number of the slot of the stator is 1 and the size of the core oil channel is 5mm×2mm,at the position of Lslot/L=0.675 for the slot oil channel,with an oil channel diameter of 1.6mm,the cooling effect is better.Compared with the tooth cooling scheme,the maximum temperature of the windings,the core part of the stator and the tooth part of the stator decreases by 41.32% in average.This structure significantly suppresses the hotspot temperature rise of the high-power density high-speed motors and provides certain reference for the heat dissipation of high-power density motors.

To address the susceptibility of purely data-driven models to measurement noise and the deviation of local state-of-health (SOH) estimates from long-term degradation trends under degraded data quality, an adaptive fusion method combining a data-driven estimator with a Generalized Power-Exponent Model (GPEM) is proposed for lithium-ion battery SOH estimation. First, multiple health features are extracted from voltage and incremental capacity curves, and a Transformer is employed to generate initial SOH observations. Then, the GPEM is constructed to characterize the nonlinear degradation trend and is incorporated into an Adaptive Extended Kalman Filter (AEKF) as the state-transition model. The measurement-noise covariance is adaptively adjusted according to residual variations, enabling recursive correction of the data-driven observations. Furthermore, the residual and AEKF estimation uncertainty are utilized by a multilayer perceptron to generate dynamic fusion weights, thereby adaptively balancing the long-term degradation trend and data-driven estimates. Experiments on the NASA dataset show that the GPEM provides better degradation-trend characterization than conventional empirical models, with a maximum mean absolute error (MAE) of 0.93%. Under white-noise and colored-noise disturbances, the maximum MAE and root mean square error (RMSE) of the complete method are 1.72% and 1.84%, respectively. Under white-noise interference, the proposed method reduces the MAE and RMSE by 56.6% and 51.2%, respectively, compared with fixed-weight fusion, and by 78.8% and 77.2% compared with KF-based fusion. In addition, cross-battery experiments under multi-battery joint training and validation on the independent CALCE dataset demonstrate that the proposed method maintains favorable SOH estimation performance across different battery individuals and data distributions, indicating a certain degree of generalization capability.

In VSC-HVDC-interconnected asynchronous partitioned power grids, quasi-synchronous frequency operation may cause disturbances in the receiving-end grid to propagate to the sending-end grid, thereby enlarging the disturbance impact area. To address low-frequency disturbances in the receiving-end grid, this paper proposes an adaptive synchronous frequency control method based on receiving-end transient support. First, a synchronous frequency control architecture incorporating the energy margin of submodule capacitors is developed, enabling the receiving-end converter station (REC) to release internal submodule capacitor energy and provide local transient energy support while maintaining DC voltage stability. Second, considering operational constraints including the submodule capacitor energy margin, capacitor-voltage lower limit, and transient overcurrent limit of converter valves, a power controllable region of the REC is established to characterize its instantaneous active-power support capability and the corresponding sustainable support time. Furthermore, a synchronous frequency control strategy for minimizing the cross-area impact of receiving-end low-frequency disturbances is proposed. The strategy prioritizes local capacitor energy to compensate for active-power deficits and adaptively activates the sending-end synchronous frequency control (SFC) for subsequent support according to the available energy margin. Simulation results show that the proposed method can effectively improve the frequency drop response of the receiving-end grid and significantly suppress the cross-area coupling impact of receiving-end power disturbances on the sending-end grid, while ensuring converter station security and DC voltage stability.

With the rapid development of renewable energy integration, transportation electrification, and industrial drives, the operating conditions of power electronic equipment are becoming increasingly complex, leading to a growing demand for intelligence in state perception, closed-loop control, operational planning, and fault diagnosis. This paper focuses on research concerning intelligent power electronic equipment and classifies control tasks into four categories according to their response-time requirements: microsecond (μs), millisecond (ms), second (s), and longer timescales. It systematically reviews how expert systems, heuristic algorithms, deep learning, deep reinforcement learning, and generative artificial intelligence can be integrated with different types of control requirements, together with representative examples and their application boundaries. The analysis indicates that intelligent algorithms are best employed in an auxiliary role for microsecond-scale control. At the millisecond scale, various intelligent algorithms can be flexibly integrated with closed-loop control, power tracking, and software fault diagnosis. At the second scale, intelligent algorithms are highly compatible with control tasks such as thermal management, mode switching, and energy scheduling. For longer-timescale applications, including health assessment, predictive maintenance, plant-level scheduling, and digital twins, existing intelligent algorithms are fully applicable, although emphasis should be placed on precisely matching each algorithm to the specific application. This paper further argues that intelligent algorithms and conventional control methods should complement and integrate with each other to leverage their respective strengths and achieve optimal control performance and operational benefits. The review provides a useful reference for the planning, selection, deployment, and application of intelligent algorithms in power electronic equipment.

With the large-scale integration of renewable energy and the widespread deployment of power electronic devices, the dynamic behavior of power systems exhibits strong nonlinearity, multi-scale coupling, and significant time-varying characteristics, posing new challenges to the accurate and efficient computation of dynamic trajectories. Traditional time-domain integration methods are computationally expensive and difficult to support fast rolling simulations, while data-driven models generally lack physical consistency and long-term stability constraints, making them prone to error accumulation and extrapolation instability. To address these challenges, this paper develops an Improved Invertible Koopman Neural Operator (I-IKNO) for power system dynamic trajectory prediction based on the concept of the invertible Koopman neural operator. The proposed method takes finite-dimensional electromechanical states, such as generator rotor angles and angular velocities, as modeling objects, and constructs a linearized representation of nonlinear states through latent-space mapping. Furthermore, considering the damping oscillation characteristics of power system transient trajectories, a state-dependent Koopman evolution structure is designed, and a high-frequency disturbance-driven branch is introduced to characterize the influence of fault switching and local fast dynamics on the dominant electromechanical modes. In this way, accurate and stable prediction of power system transient trajectories can be achieved. Finally, the effectiveness of the proposed method is verified on a modified IEEE 39-bus system. The results show that the proposed method significantly improves the prediction efficiency and stability of dynamic trajectories while maintaining high computational accuracy.

To address the issues of low accuracy and poor generalization in state of health (SOH) estimation for energy storage batteries under dynamic operating conditions, this paper proposes a dynamic adaptive supervised contrastive transfer learning framework. This framework aims to resolve the prediction drift problem of traditional fine-tuning strategies and the feature semantic mismatch caused by unsupervised domain adaptation. Firstly, a lightweight ResNet-1D backbone network is designed to adapt to small-sample private data. Secondly, a supervised contrastive mechanism based on SOH binning is introduced to extract essential aging features by explicitly utilizing label information, thereby eliminating "false negative" interference. Finally, Multi-kernel Maximum Mean Discrepancy is employed to establish a multi-scale distribution generalization boundary. Cross-domain experiments demonstrate that the proposed method achieves a root mean square error of 0.68% in leave-one-out validation, with the maximum absolute error strictly controlled within 1.85%, significantly outperforming the 7.80% achieved by traditional fine-tuning methods. This effectively eliminates the risk of non-conservative estimation. The proposed method can effectively enhance the accuracy and generalization capability of SOH estimation for energy storage batteries, ensuring safe operation and maintenance throughout their entire lifecycle..

Metallized film capacitors (MFCs) are widely used in various fields such as power systems, electronic devices, and electric vehicles due to their excellent energy storage properties. However, research on the aging characteristics of MFC films under coupled electrical-thermal stress and the mechanisms of charge conduction remains insufficient. This paper conducts combined electrical-thermal aging experiments on MFCs at different temperatures, measuring key performance parameters such as leakage current, capacitance, and dielectric loss under varying electrical-thermal conditions. The roles and dominant mechanisms of space-charge-limited conduction (SCLC), hopping conduction, and the Poole–Frenkel effect under different electric fields and temperatures are systematically analyzed. The results indicate that when the aging temperature exceeds the rated temperature (85 °C), the leakage current increases significantly with rising temperature. Capacitance degradation mainly depends on the clearing area of the metallized electrode layer during self-healing. When the applied voltage reaches four times the rated voltage, the dielectric loss increases by orders of magnitude, accompanied by a synchronous rise in equivalent series resistance (ESR). The enhancement of SCLC, hopping conduction, and the Poole–Frenkel effect induced by increased electric field and temperature are identified as critical microscopic factors contributing to the degradation of MFCs.

Rotor surface-mounted magnet detachment or fragmentation is one of the core challenges faced by ultra-high-speed permanent magnet machines. Applying prestress through a carbon fiber winding sleeve to ensure rotor integrity is a key approach to improving operational reliability. To meet the increasingly stringent requirements for sleeve reliability and compactness in ultra-high-speed applications, this paper comprehensively considers rotor deformation and interlayer interactions in carbon fiber composites, and proposes a convergent prestress calculation model for wound sleeves. The minimum prestress required for the rotor under different operating conditions is analyzed, and the iterative process between convergent prestress and residual winding tension is examined, along with the prestress under different design parameters. With the objective of ensuring rotor reliability under operating conditions and optimizing sleeve design parameters, an iterative design procedure for the sleeve parameters is established. Finally, an optimized sleeve design is performed for a rotor operating at 150 000 r/min under room-temperature conditions. Additional sleeves with different design parameters are fabricated, and a strain measurement platform is established to measure radial strain. The results validate the consistency and feasibility of the proposed convergent prestress calculation model and the design parameter iteration procedure.

When facing external disturbances such as uncertainties in new energy output and extreme weather events, local weak power grids are highly susceptible to widespread load loss or even system collapse. Multi-type energy storage resources, represented by electrochemical energy storage stations, hydrogen energy storage stations, and mobile energy storage vehicles, can regulate the power and energy balance of local weak power grids across multiple time scales and spatial ranges. First，this paper establishes a power constraint set for local weak power grids that accounts for the source-grid-load-storage elements, and proposes a general model for multi-type energy storage resources. Second, weak nodes in local weak power grids are identified under extreme weather disturbances to enhance the specificity of fixed energy storage resources allocation. Subsequently, considering the economic costs of power grids operation, a collaborative planning model for fixed and mobile energy storage resources is constructed. A stepwise solution approach is adopted, employing a particle swarm optimization algorithm for fixed storage resources and a column-and-constraint generation algorithm for mobile storage resources, respectively. This yields a coordinated configuration scheme for multi-type energy storage resources tailored to local weak power grids. Finally, the effectiveness of the proposed scheme in reducing the load loss risk of local weak power grids is validated based on a modified IEEE RTS 79 system.

The capacity allocation of converter valves directly determines the reliability and economy of high-voltage direct-current (HVDC) transmission systems. To tackle the issue of capacity redundancy arising from diverse sending-end requirements in multi-infeed HVDC systems, this paper focuses on the Controllable Line-Commutated Converter (CLCC) and investigates its loss modeling and capacity optimization method. Firstly, a unified loss calculation model for CLCC is established and validated through simulations. On this basis, a strongly nonlinear optimization model is constructed with capacity allocation as the core and multiple constraints taken into account. Furthermore, a genetic algorithm is adopted for global parameter tuning, which effectively coordinates the coupling relationship between redundant configuration and operating performance. Results demonstrate that the proposed method can significantly reduce system losses, improve equipment utilization and operational economy, and provide a theoretical basis for capacity optimization design in CLCC engineering applications.

To enhance the mobility of a bipedal wheeled robot over rugged terrain, this paper proposes a locomotion strategy based on Adaptive Super-Twisting Sliding Mode Control (ASTSMC). In view of the dual-input multi-output characteristics of the bipedal wheeled robot, dual sliding surfaces are designed to achieve coordinated control of the system. An equivalent adaptive gain mechanism based on posture deviation is introduced, which effectively reduces the chattering problem inherent in conventional super-twisting sliding mode control in practical applications, while also improving traversability over uneven terrain. The stability of the proposed method is rigorously proved using the Lyapunov stability theorem, ensuring theoretical reliability of the control strategy. Experimental results demonstrate that, compared with the Linear Quadratic Regulator (LQR), the proposed approach significantly reduces posture angle fluctuations when traveling over uneven ground and effectively suppresses the chattering associated with Super-Twisting Sliding Mode Control (STSMC), confirming the effectiveness and superiority of the method.

Aiming at the limited support capability of coastal power grids, which makes it difficult to accommodate the centralized integration of large-scale deep-sea offshore wind power, and the issue that power fluctuations during long-distance wind power transmission may affect system stability, this paper proposes a four-terminal flexible DC grid-connected system considering multi-energy power compensation. In the proposed system, deep-sea offshore wind power, an onshore wind–PV–nuclear–pumped storage energy integration station, and inland power grids are interconnected through a multi-terminal flexible DC network, enabling long-distance transmission of deep-sea offshore wind power and coordinated compensation by onshore multi-energy resources. Considering the output characteristics of nearshore wind power, photovoltaic power, nuclear power, and pumped storage, a wind–PV–nuclear–pumped storage capacity configuration method for constant-power transmission is established to clarify the power compensation capability and operating boundary of the onshore energy integration station. On this basis, an additional outer-loop control strategy based on the three-degree control architecture of the hybrid MMC is proposed, which does not rely on high-speed communication and actively compensates for deep-sea offshore wind power fluctuations by using DC current deviation. Corresponding fault ride-through strategies are further designed for different types of DC faults. Finally, a multi-terminal grid-connected system model for deep-sea offshore wind power is built on the PSCAD/EMTDC simulation platform. The simulation results verify the effectiveness and feasibility of the proposed topology, control strategy, and DC fault ride-through strategies.

Partial discharge is an early indication of insulation failure in transformers. Although the ultra-high frequency (UHF) detection method has high sensitivity and strong anti-interference capability, it is limited by the installation position and method of the sensor, which restricts its application in the detection of partial discharge in transformers. Therefore, this paper proposes a partial discharge detection method based on the measurement of UHF signals from the grounding current of the core. Through electromagnetic simulation, the propagation and coupling characteristics of UHF signals within the transformer were calculated, the feasibility of the core grounding current UHF sensing method was analyzed, and three frequency bands (391-600  MHz, 1.34-1.6  GHz, and 2.2-2.77  GHz) were identified as suitable frequency bands for PD detection. Furthermore, a signal extraction device was designed, and comparative tests under different defect conditions were carried out, comparing with the traditional UHF method and the pulse current method. The results show that in combination with the main radiation frequency band of the partial discharge electromagnetic wave under different PD defect conditions, the grounding current of the core carries effective partial discharge UHF signals within the band of 391-600  MHz. The method proposed in this paper has good consistency with the traditional UHF detection results, with a linear correlation coefficient of the signal amplitude reaching 0.89 and a similarity of the PRPD spectrum exceeding 0.95. In addition, this method does not require additional built-in sensors, is easy to install, and has good anti-interference performance, meeting the actual needs for online detection of partial discharge in operating transformers.

Under the "dual-carbon" goals, farm power supply is confronted with dual challenges of renewable energy integration and electric farm machinery charging. This study proposes two optimization models for the coordinated planning of farm electrification and distribution systems. First, to address the charging load and charging facility configuration of electric farm machinery, a Monte Carlo simulation method based on operational seasonality is developed to generate charging load profiles. A model for charging facility type and quantity configuration is established, employing the Big-M method to linearize occupancy constraints for optimal charging scheduling, thereby determining the optimal deployment of AC/DC charging stations. Second, for farm distribution network planning, a bi-level optimization model considering renewable energy generation and charging loads is formulated. The upper level optimizes charging station and line layout to minimize annual planning costs, while the lower level minimizes operational costs by optimizing farm machinery charging scheduling and biomass power output allocation. Results demonstrate that the proposed method enhances the economic viability of system planning and provides a foundation for farm electrification development.

Oil immersed transformers are key equipment in power systems. Arc discharge is the most serious discharge fault inside transformers, which can easily cause combustion and explosion accidents. The discharge optical detection method has unique advantages in detection speed, anti-interference ability, and high and low voltage isolation, which helps to improve the recognition speed and accuracy of internal discharge faults in transformers. Based on this, this article takes a typical S13-M400/10-0.4 three-phase oil immersed distribution transformer as the research object, studies the propagation characteristics of optical signals inside the transformer, establishes a simulation model of transformer optical propagation characteristics, and obtains an optical sensor layout method based on the propagation characteristics of power frequency arc light in oil. The research results indicate that the irradiance distribution on the walls of the fuel tank is highest on the direct surface closest to the light source, which can reach 160W/m2. Depending on the type of sensor, it is necessary to cover the projection area of the winding on the front and side of the fuel tank as much as possible, or arrange it in the center of the projection area, in order to achieve the best detection effect.

The intense vibration caused by short-circuit current impact on transformer windings is a key factor leading to mechanical damage in equipment. Accurately calculating the vibration behavior of windings is crucial for transformer condition assessment. This paper proposes a transient vibration model that accounts for the axial coupling effects of transformer windings. The results indicate that the uneven distribution of clamping force, caused by variations in spacer thickness, leads to deviations of approximately 5.6% and 7% from design standards for high-voltage and low-voltage windings, respectively. Compared to the independent vibration calculation model, the vibration amplitudes of the high-voltage and low-voltage windings calculated by the dual-winding coupled vibration model vary by 9.5% and -8.7%, respectively, while the dynamic compressive stress of spacers changes by 4.9% and -4.5%. The coupled model considers two influencing factors: uneven clamping force distribution and dynamic vibration imbalance. As the clamping force decreases, both winding vibration amplitudes and spacer dynamic compression stress exhibit an increasing trend. With rising vibration imbalance, the deviation from the calculation results of the independent vibration model gradually grows. In conclusion, this study provides a more precise computational method for analyzing winding vibration characteristics and holds significant engineering application value.

Insulated Gate Bipolar Transistors (IGBTs) are widely used in wind power, rail transit, and high-voltage direct current (HVDC) transmission, and their reliability is crucial for the safe and stable operation of these fields. Therefore, constructing accurate IGBT module lifetime assessment models, clarifying the degradation mechanisms of IGBTs, and researching effective junction temperature monitoring methods are of significant importance for predicting the remaining service life and system stability. First, the degradation mechanisms and main failure modes of welding-type and press-pack IGBTs are discussed. Then, according to the main process of IGBT lifetime assessment, several common junction temperature monitoring techniques are introduced, the challenges of internal junction temperature monitoring are explored, junction temperature monitoring methods are outlined, and commonly used model-based and data-driven lifetime assessment technologies are described. Finally, addressing the current research deficiencies and combining the current status of IGBT lifetime assessment research, the development trends of IGBT module lifetime assessment methods are discussed.

Rotor dynamic eccentricity is one of the most common fault types in large steam turbine generators, and the degree of dynamic eccentricity has a positive correlation with the vibration state of the unit. This paper addresses the complex power scenarios faced by steam turbine generators during operation. Based on the magnetic field asymmetry caused by dynamic eccentricity faults, it derives the variation patterns of unbalanced electromagnetic forces resulting from changes in the orientation of the main magnetic field and harmonic magnetic fields. Furthermore, finite element simulations are used to obtain the distribution characteristics of unbalanced electromagnetic forces in steam turbine generators under different active power and reactive power conditions. Unbalanced electromagnetic forces are a significant cause of vibration. The research in this paper contributes to the accurate prediction of the vibration state and development trends of steam turbine generators under various power conditions, providing decision support for the assessment of unit operating conditions.

The high proportion of distributed photovoltaic access to active distribution networks exacerbates the mismatch between source and load space, and traditional fixed energy storage is limited by installation location, making it difficult to achieve global optimal consumption. Therefore, this article proposes an active low-carbon economic dispatch strategy for distribution networks that takes into account the spatiotemporal coordination of mobile energy storage. Firstly, a traffic network accessibility matrix that takes into account the actual travel time of roads is constructed using graph theory algorithms, and the shortest path algorithm is introduced to construct the electrical distance matrix of the entire network, finely characterizing the spatial transfer boundary of mobile energy storage and the cost of power grid topology scheduling; At the same time, taking into account the time of use electricity pricing mechanism, power flow safety of the distribution network, and battery operation constraints, a scheduling model with the lowest comprehensive operating cost of the system is constructed. Secondly, in response to the strong coupling between position and power in the model of mobile energy storage, a 0-1 state variable and the large M method are introduced for linearization reconstruction, transforming the original problem into a mixed integer linear programming model and achieving global solution. Finally, a comparative example based on the IEEE 33 node system shows that mobile energy storage effectively breaks the topological limitations of consumption by leveraging the spatiotemporal flexibility of "active light chasing" and "on-site support"; Compared with fixed energy storage with the same capacity, the proposed strategy reduces the penalty cost for photovoltaic curtailment by 50.25%, lowers the total operating cost of the system by 11.33%, and smooths out the peak valley difference of the main online grid. This study provides an effective approach for the source grid storage coordination and low-carbon economic operation of high proportion new energy distribution networks.

To address the problem of insufficient inertia support in grids with a high proportion of renewable energy, this paper proposes a novel inertia flywheel system based on an electromagnetic coupler and its control strategy. In this system, a squirrel-cage induction motor acts as the electromagnetic coupler, providing magnetic coupling between the flywheel rotor and the rotor of a synchronous motor. A back-to-back converter governs the torque transmission, allowing the flywheel to store and release energy over a wide speed range while the synchronous motor remains connected to the grid at a constant speed. Discrete mathematical models of the key components are established, and a model predictive control (MPC) strategy is designed for power tracking and flux linkage control. The control strategy is validated through MATLAB/Simulink simulations. Simulation results demonstrate that the proposed inertia flywheel system exhibits superior dynamic performance under active power output conditions and strong stability under reactive power output conditions.

The suburban railway power supply system (SRPSS) frequently suffers from an excessively low power factor due to the capacitive reactive power generated by long-distance high-voltage cables, leading to substantial power factor penalties. To address this problem, this paper investigates the H integrated substation on the S1 line of the SRPSS. Measurement data indicate that the 110 kV cable is the dominant source of capacitive reactive power. A reactive power compensation strategy based on optimal line loss and hierarchical coordination is proposed. Specifically, a fixed shunt reactor is installed on the 110 kV side to compensate for the fixed portion of capacitive reactive power, while the existing magnetically controlled reactor on the 20 kV side provides dynamic compensation. An optimal line-loss coordination model is formulated, taking into account the non-switching constraint of the fixed equipment on the high-voltage side, and a genetic algorithm is adopted to determine the optimal output of the magnetically controlled reactor. Simulation and economic analysis demonstrate that the proposed method maintains the power factor at the grid metering point near unity and significantly reduces line losses. With an investment payback period of approximately 3.5 months, the method proves effective in enhancing both the operational security and economic performance of the system.

To address the challenges of effective online monitoring and accurate recognition of winding deformation in converter transformers, this paper proposes a GWO-CNN based identification method incorporating optimal leakage magnetic field characteristics. The causes of early-stage deformation are analyzed theoretically, and the leakage magnetic field distribution is validated through dynamic model experiments, leading to a highly credible finite element model of the converter transformer. Subsequently, focusing on the leakage magnetic field distribution characteristics under typical early-stage compression and bulging deformations, an initial feature set comprising 20 dimensional features is established. The Recursive Feature Elimination algorithm based on Random Forest (RF-RFE) is employed for adaptive optimization of the feature space. By quantitatively evaluating feature contributions, redundant information is eliminated, and the optimal feature subset most sensitive to early-stage deformation faults is identified. Based on this, the Grey Wolf Optimizer (GWO) is utilized for adaptive optimization of Convolutional Neural Network (CNN) hyperparameters, establishing the GWO-CNN fault diagnosis model. Case study results demonstrate that feature optimization significantly enhances computational efficiency, with the model achieving a diagnosis accuracy of 97.62%.

Accurately predicting the Remaining Useful Life (RUL) of Insulated Gate Bipolar Transistors (IGBTs) is critical to ensuring the reliable operation of modern power electronic systems. This paper proposes an IGBT remaining useful life prediction model based on one-dimensional Kalman filtering and Bidirectional Long Short-Term Memory network (Bi-LSTM). Firstly, the one-dimensional Kalman filtering algorithm is introduced to denoise and smooth the original sequences collected by sensors, so as to restore the real physical trend of device aging. Secondly, the sliding time window mechanism is used to construct sequential feature inputs, and the Bi-LSTM network with two parallel computing paths in forward and backward directions is adopted to achieve deep feature fusion of historical cumulative damage and global temporal context information. The experimental results show that the proposed Bi-LSTM model exhibits excellent performance in prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of only 0.0686. Finally, combined with the Coffin-Manson model, the effectiveness and high precision of the proposed method in IGBT lifetime prediction are verified.

Accurate short-term output prediction of run-of-river small hydropower is the key technical support to improve the consumption level of clean energy. However, the output of run-of-river small hydropower is strongly coupled with factors such as rainfall, soil moisture, and runoff evolution, exhibiting extremely strong randomness and volatility. Aiming at the problems of insufficient nonlinear characterization of existing physical models and the easy distortion of data-driven models lacking physical constraints, an output prediction method for run-of-river small hydropower based on runoff estimation and physical conservation constraints is proposed. First, by analyzing the energy conversion mechanism of run-of-river small hydropower, a refined output model considering hydraulic loss correction and the nonlinear attenuation characteristics of turbine efficiency in the low-load region is constructed. Subsequently, a run-of-river small hydropower output prediction architecture based on PC-LSTM-Attention is built, and the antecedent precipitation index is introduced to dynamically quantify the initial soil moisture state of the basin, thereby mining the spatial heterogeneity and long-span time-delay effects of the runoff generation and concentration process. Finally, a composite loss function embedded with water balance and non-negativity criteria is constructed, and the physical consistency and prediction accuracy of the model are improved by optimizing the backpropagation path. Case validations show that the proposed method can effectively capture the strong random fluctuation characteristics of small hydropower output, suppress prediction deviations under extreme working conditions, balance prediction accuracy and physical rationality, and provide a reliable basis for the optimal scheduling of run-of-river small hydropower grid connection.

Due to its intuitive logic and multi-objective constrained collaborative control, MPC has become a research hotspot in PMSM control. To address the parameter mismatch in conventional MPCC, this paper establishes an ARX data-driven model for PMSM, whose coefficients are identified using the RLS algorithm. A purely data-driven model-free predictive control approach is used to mitigate parameter mismatch. Aiming at the overshoot–response time contradiction in conventional MPCC speed loops, an optimized predictive function speed controller is designed. Single-step prediction reduces computational complexity, and a LESO is employed for disturbance compensation. Replacing the traditional PI controller, the proposed controller resolves the inherent performance trade-off. Cascading the two improved controllers integrates their merits. Experimental results validate that the proposed scheme resolves parameter mismatch and PI performance contradiction, suppresses current and torque shocks, and effectively improves the parameter robustness and steady-state performance of the system.