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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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..

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.

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.

To address voltage violations and insufficient grid-connection strength in remote weak power systems under high penetration of renewable energy and impulsive loads, this paper proposes a bi-level optimization method for coordinated photovoltaic (PV) and energy storage system (ESS) configuration considering the Multiple Renewable Energy Stations Short-Circuit Ratio (MRSCR) constraint. In the upper-level model, a second-order cone programming (SOCP) formulation based on the DistFlow power flow model is developed, aiming to minimize the annual total cost and determine the optimal siting and sizing of PV and ESS. In the lower-level model, the Newton–Raphson (NR) power flow method is employed to accurately verify the optimization results, and a cutting-plane strategy is applied to iteratively refine voltage constraints, thereby improving power flow accuracy while maintaining computational efficiency. Case study results indicate that, under a PV-only configuration, the system short-circuit ratio tends to fall below the security threshold, leading to insufficient grid-connection strength. By introducing energy storage and performing coordinated configuration, the short-circuit ratio at grid-connection points can be increased while reducing the required PV capacity, thereby enhancing system disturbance tolerance and renewable energy accommodation capability. Extended tests on the IEEE 39-bus standard system further verify the solution feasibility, computational efficiency, and applicability of further strengthens grid-connection strength while ensuring voltage stability, providing a useful reference for PV–ESS planning in remote areas with high penetration of renewable energy.

With the development of smart grids, distribution systems have gradually evolved into cyber-physical distribution systems (CPDSs), where communication networks are tightly coupled with physical distribution networks. However, as a large number of distribution components enter the aging stage, aging-related failures have become a major threat to power supply reliability. Existing reliability studies have difficulty accurately characterizing the state transition characteristics of both aging-related and random failures simultaneously, leading to deviations in the reliability assessment of aging CPDSs. In addition, most existing reliability enhancement studies considering cyber-physical coupling focus on post-fault passive responses, while limited attention has been paid to proactively improving reliability by strengthening communication support. To address these issues, this paper proposes a multi-energy-source configuration-based reliability enhancement strategy for CPDSs under multiple failure modes. First, a three-state component reliability model is developed to accurately characterize component failure risks by simultaneously considering aging-related and random failures. Then, considering cyber-physical coupling characteristics and the role of uninterruptible power supplies in supporting communication equipment, a reliability enhancement model based on multi-energy-source configuration is established to proactively reduce the expected energy not supplied (EENS). Finally, the proposed model is solved using linearization techniques and sequential Monte Carlo simulation and tested on modified IEEE 33-bus CPDSs under different aging scenarios. Simulation results demonstrate the effectiveness and superiority of the proposed strategy in improving power supply reliability.

Short-term bus load forecasting is an important foundation for the secure operation of modern power systems and day-ahead spot-market clearing. Compared with aggregated loads at the regional or system level, bus loads are closer to node-level operating conditions, where long-term trends, intraday cycles, and short-term random disturbances are directly superimposed within the same series, resulting in more pronounced multi-timescale coupling and greater forecasting difficulty. To address this issue, a short-term bus load multi-timescale decoupling and forecasting method based on Symplectic Geometry Mode Decomposition (SGMD) is proposed. First, SGMD is used to adaptively decompose the original load series while preserving the phase-space structure, thereby achieving multi-timescale feature decoupling and reducing sequence complexity. Then, CNN-BiLSTM is employed to jointly model the local features and temporal dependencies of each component, and an attention mechanism is introduced to achieve adaptive weighted fusion of multi-component features for forecasting the next-day bus load curve. Results on measured data show that the proposed method achieves a MAPE of 4.92% and outperforms multiple baseline models, verifying its effectiveness and demonstrating good engineering application value.

Non-invasive neuromodulation has shown promising potential for the intervention of neurological and psychiatric disorders, and multimodal combined stimulation has emerged as a key research direction in this field. To investigate the potential synergistic neuromodulatory effects of two physical stimuli—low-intensity pulsed magnetic fields and auditory stimulation—this study developed a parameter-adjustable combined magnetic-auditory stimulation system for mouse experiments. The system comprises an upper computer, a control module, a magnetic-field module, an acoustic module, and four independent sound-insulated chambers, enabling the synchronous and combined output of modulated pulsed magnetic fields and rectangular-wave-modulated bipolar acoustic stimulation signals. The magnetic-field module adopts a double-wound solenoid structure, supports switching between active and sham stimulation, and can generate modulated pulsed magnetic fields with adjustable repetition frequencies of 1–5 Hz, carrier frequencies of 20–100 Hz, and amplitudes of 0–20 mT. System tests demonstrated that the device achieved accurate synchronous and combined output of magnetic and acoustic stimulation, with reliable hardware protection and good thermal stability during long-term operation. The four sound-insulated chambers support parallel experiments involving control, single-stimulation, and combined magnetic-auditory stimulation groups, providing a reliable animal experimental platform for multimodal neuromodulation studies in neurological and psychiatric disorders.

In recent years, with the development of high‑altitude applications, such as high-altitude airships and unmanned aerial vehicles, the high-altitude propulsion inverters based on SiC MOSFETs have become essential for enhancing the system performance. SiC MOSFETs have high performance in efficiency and reliability, however, their switching behavior changes in the extremely low and wide-range temperature conditions, which influences the VGS turn-off spike. In this paper, the model and behavior of SiC MOSFETs in wide operating temperature range are studied, revealing the mechanism of VGS spike formation. Based on the analysis, a NMOS-based active clamping gate-dive method is proposed to suppress the VGS turn-off spike and reduce the turn-off energy loss. The proposed method is evaluated through double-pulse simulation test in the temperature range of -80 ℃~+80 ℃ and the experimental test in the temperature range of -30 ℃~+40 ℃. The results verify the effectiveness of the proposed method for enhancing the efficiency and reliability of SiC MOSFETs in wide‑temperature high‑altitude applications.

This paper discusses high-flux concentrating and high-temperature receiver technologies for the collector system of fourth-generation solar thermal power generation. The key technologies investigated include adaptive concentrating technology with an average flux density higher than 600 kW/m2 at the receiver aperture, as well as receiver technology operating at 800 ℃. Through analysis of concentrator errors, receiver materials, receiver structures, and other core technologies in the collector system, an efficient collector technology scheme for fourth-generation solar thermal power generation is proposed. The results show that optimized design, high concentration ratio, and adaptive optical technology can effectively improve concentrator performance and meet the requirements of fourth-generation solar thermal power generation systems for efficient and stable solar concentration. In addition, solid particle receivers can satisfy the requirement of an 800 ℃ receiver temperature for fourth-generation solar thermal power generation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.