Concentrating solar power (CSP) is endowed with the capabilities of large-scale deployment, cost-effectiveness, and high-safety long-duration thermal energy storage, and generates electricity through steam turbine generator. It can replicate the grid support and regulation functions of coal-fired power plants, demonstrating significant potential as a green, low-carbon base load power source. As an effective approach to achieve the safe and reliable replacement of traditional energy with renewable energy, CSP serves as a robust underpinning for enhancing the security and flexibility of power supply.To provide a comprehensive overview of CSP technologies and the current status of the industry, and to further advance the development of the CSP sector in China, this paper summarizes the progress of China’s CSP industry in 2025. The key aspects covered include: an overview of CSP technologies, market development status, operational performance of CSP demonstration projects, industrial chain layout, R&D advancements in CSP technologies, economic viability of CSP technologies, and carbon emission reduction benefits of CSP. Finally, targeted recommendations for the future development of the CSP industry are proposed.

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

This paper conducts a comparative study on virtual impedance and virtual admittance control strategies for grid-forming static var generators (SVGs). An SVG impedance model is established using the harmonic state-space (HSS) method to analyze small-signal stability, with frequency scanning validation implemented through a simulation platform. By deriving the inner-loop transfer functions, similar equivalence conditions between the two control strategies are identified. The impedance differences of grid-forming SVGs under both control methods are systematically investigated from a small-signal stability perspective, along with their adaptability to varying grid strength conditions. Based on the stability analysis results, parameter design principles for virtual impedance and admittance are proposed. Furthermore, leveraging the fundamental differences between the two strategies, the virtual impedance control method is optimized to enhance its stability in strong grid scenarios. Finally, a hardware-in-the-loop (HIL) experiment is conducted on the RT-LAB platform to validate the effectiveness of the theoretical analysis and optimization method proposed in this study.

The high proportion of renewable energy integration in distribution systems under the dual-carbon strategy has introduced power quality issues such as voltage violations, harmonic distortions, and three-phase imbalances. Grid-forming converters, with their active regulation capabilities, provide a feasible technical solution to address these power quality challenges. This paper focuses on the research progress in power quality enhancement technologies for grid-forming converters in high-penetration renewable energy systems and their regulation capability evaluation. First, grid-forming control strategies for power quality improvement are analyzed from two perspectives: individual converter control and cluster operation. Subsequently, evaluation methods for assessing the regulation capability of grid-forming converters are discussed, including inertia support and other key performance metrics in renewable energy-integrated grids. Furthermore, solutions for mitigating different power quality issues using grid-forming converters are systematically reviewed. Finally, future research directions for grid-forming converters in high-penetration distribution networks are outlined, providing theoretical support for power quality enhancement in high-proportion renewable energy systems.

The traditional fixed-parameter virtual synchronous generator (VSG) control strategy is difficult to maintain when it comes to ensuring long-term stable operation in the face of grid frequency disturbance and active output reference value disturbance. This strategy facilitates the multi-parameter adaptive optimal control of VSG by constructing a mathematical model of VSG and its adaptive control model. The exponential function is adopted to optimize the inertia and damping coefficients of VSG, and the tanh function is introduced to optimize the control of active sag coefficients. On the basis of these functions, and considering the process of angular frequency oscillation, the multi-parameter adaptive optimal control of VSG is realized. In conclusion, the simulation analysis and hardware in the Loop (HIL) experiment demonstrate the efficacy of the strategy proposed in this paper in enhancing the support of the grid frequency and ensuring the stable operation of the system.

To facilitate the low-carbon transition of offshore oil and gas fields, utilizing marine renewable energy becomes an important development direction for the power supply of offshore oil and gas platforms. Wind power attracts widely attention owing to its advantages of rich resources and high power generation efficiency. However, the fluctuation and intermittency may cause frequency fluctuation during grid connection. The problem becomes more serious with the gradual exit of gas turbines, leading to the reduction of inertia and frequency response resource. Aiming at the problem of frequency stability and wind power utilization in the integration of high percentage wind power into offshore oil and gas grid, a collaborative configuration method of grid-forming and grid-following energy storage systems is proposed. The frequency response characteristics of system with grid-forming energy storage are analyzed. The models of grid-forming energy storage, grid-following energy storage, gas turbine generator, and wind turbine are constructed. An energy storage configuration optimization model aimed at minimizing the average daily cost is constructed, considering operational constraints and frequency constraints induced by wind power output fluctuations as disturbance power. By applying the convex hull relaxation method, constraint linearization is achieved, enabling efficient solution of the energy storage configuration. Degradation of BESS for the configuration scheme is checked. Case studies demonstrate that the proposed method can collaboratively configurate grid-forming and grid-following energy storage. The method substantially enhances frequency stability and wind power consumption, and reduces system costs.

Overheating caused by electrical contact failure of tulip contacts is a common fault in switchgear. Traditional temperature detection techniques are difficult to directly observe and measure the hot spot temperature of the tulip contact in switchgear by going deep into the equipment. It is of great significance to use mechanical contact pressure to predict the hot spot temperature of the tulip contact. This article calculates the contact resistance through the contact pressure of the tulip contact, constructs a pressure-electromagnetic-heat-flow multi-physics coupling simulation model, and analyzes the effects of fault current, ambient temperature, and contact pressure on the temperature rise of the tulip contact. Finally, a reasonable orthogonal experiment is designed to generate a sample database of tulip contacts in switchgear. Multiple physical field coupling simulation data is used as training samples to establish a support vector regression model for predicting the hot spot temperature of tulip contacts. The research results can provide theoretical basis and technical support for the accurate prediction of the hot spot temperature of the tulip contacts of circuit breakers in KYN type switchgear and the intelligent operation and maintenance of equipment.

Household all vanadium flow battery energy storage systems for photovoltaic-storage applications are difficult to assess the energy loss economics due to the low average system efficiency under operations such as intermittent power supply, hot standby, and multiple applications, and the unbalanced distribution of energy consumption. This paper proposes a dynamic assessment method for energy loss of household all vanadium batteries, which includes loss calculation methods for flow resistance, battery polarization, ion crossing, branch current and auxiliary thermal management. The paper takes 10 kW vanadium flow battery equipment as the test research object to analyze the distribution of each energy loss parameter of the battery over time for multiple charging and discharging experiments in the constant-voltage, constant-power charging and discharging modes under photovoltaic access and to establish a loss assessment. The probability distribution function of the energy loss rate and SOC/SOD is established to evaluate the energy loss interval during the long charge/discharge cycle of the household system. The results show that the energy loss of the household system varies linearly with SOC between 266% and 402%, and the flow resistance loss accounts for 6%~7% of the interval in the long cycle operation, with the largest value near SOC=25%; the battery polarization loss accounts for 8%~9% of the interval, with the largest value near SOC=85%; the ionic cross-loss accounts for 1%~2% of the interval, with the largest value at the end of the charging and discharging period when SOC=75%; and the branch circuit loss accounts for 1%~2% of the interval. The loss value is largest near SOC=75% at the end of charging and discharging period; the branch circuit loss accounts for 4%~5% of the interval, and the loss value is largest near SOC=85%; and the loss of auxiliary thermal management equipment accounts for 5%~6% of the interval in the high temperature environment in summer, and the loss value is largest near SOC=25%. This study provides experimental guidance for the operation optimization of the optical storage and charging multi-scenario vanadium flow battery energy storage system, and the experimental data provide effective support for the improvement of system parameter design.

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

The Grid-forming converter(GFM) face a contradiction between transient stability and power quality under asymmetric faults. The currently dominant fault ride-through (FRT) method based on command switching fails to effectively address the issues of suppressing power double-frequency components and reducing current asymmetry. To resolve these problems, this paper proposes a multi-objective optimal control method. First, by analyzing the modification mechanism of power loop control parameters, it is clarified that the reasonable design of commands and control variables under faults is critical to achieving smooth FRT. Second, the active power reference, voltage reference, and damping parameters of GFM under symmetric faults are integrally adjusted to enhance their transient stability. Next, the impact of asymmetric faults on the transient characteristics of GFM is analyzed. Building on the symmetric FRT method, smooth FRT under asymmetric faults is realized by additionally applying the integral feedback of angular frequency deviation upon detecting negative-sequence components. Meanwhile, a multi-objective strategy for suppressing power double-frequency components and balancing current asymmetry is designed using fuzzy control to select the current reference in the negative-sequence loop. Finally, simulation results verify that the proposed method enhances FRT performance and improves the flexibility in power quality.

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

When the robot joint motor runs under high overload conditions, the short-term high temperature rise at the end winding will endanger the insulation of the motor winding, which will bring hidden dangers to the operation performance and reliability of the permanent magnet motor. In this paper, a transient equivalent thermal network modeling method based on lumped parameter thermal network method (LPTN) is proposed to solve the transient short-term high temperature rise of a permanent magnet motor with epoxy potting structure at the winding end under high overload conditions. The method considers the change of epoxy specific heat capacity with time and the equivalent heat capacity of each node, and deduces the iterative calculation formula of transient temperature by finite difference method. Based on the proposed model and calculation method, the instantaneous temperature rise characteristics under natural convection and epoxy encapsulation of end-winding were calculated respectively for the short-time operation of the prototype under 4 times overload, and the simulation results were compared with the Computational Fluid Dynamics(CFD) method. In addition, the effects of the thermal conductivity and overload time ratio on the temperature characteristics of epoxy were analyzed by using the established model. Finally, through the overload temperature rise experiment of the prototype, the rationality of the transient thermal network modeling method and the accuracy of the transient temperature calculation method are proved.

The inherent fluctuations and low inertia characteristics of current renewable energy generation systems lead to degraded frequency stability in power systems. Conventional DC capacitance inertia-synchronization control methods, which lack damping components, tend to cause system oscillations under disturbances. To address this issue, this paper proposes a feedforward damping strategy suitable for grid-forming converters in flexible DC receiving-end systems. By introducing DC-side voltage deviation and damping coefficients to construct feedforward damping, this strategy can compensate for the missing damping components in control systems. Through comparative analysis of small-signal models with/without damping components can regarding inertia, synchronization coefficients, and damping characteristics, we demonstrate that this strategy can effectively provide system with damping. The mathematical model of feedforward damping aligns with the physical principles of synchronous machines, overcoming the interpretability limitations of existing research methods while facilitating parameter matching and ensuring full-frequency applicability, thereby enhancing model universality. Simulation models and experimental platforms for flexible DC grid-connected systems were established, with results confirming that the proposed feedforward damping control strategy effectively suppresses system oscillations under various disturbance conditions.

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

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

Harmonic currents significantly increase the harmonic losses and temperature rise in transformer windings. Quantifying the impact of harmonic currents on transformer temperature distribution and thermal aging life is crucial for accurately modeling transformer lifespan. This paper investigates the temperature distribution and thermal aging life of transformer windings under the influence of harmonic currents. The study is structured as follows: First, a simulation model for an 110 kV oil-immersed AC transformer, incorporating electromagnetic, thermal, and fluid flow fields, is constructed to analyze the temperature distribution characteristics of the windings under different load currents and harmonic currents. Second, a quantitative calculation model for winding hotspot temperature, considering the influence of harmonic currents, is developed and experimentally validated. Finally, an improved thermal aging life model for oil-paper insulation, accounting for harmonic currents, is proposed to quantify the impact of harmonic currents on thermal aging life. The results indicate that the winding hotspot temperature increases exponentially with the load factor and decreases exponentially with the inlet flow rate. The maximum error percentage between the simulated and measured temperatures under power frequency currents is 1.06%, while the maximum relative error between the simulated and calculated hotspot temperatures under harmonic currents is 0.855%. The thermal aging life of the windings decreases exponentially with the increase in harmonic current frequency and content. When the 250 Hz harmonic current content increases from 5% to 30%, the reduction percentage in the thermal aging life of the oil-paper insulation reaches 97.02%. The findings provide a scientific basis for addressing temperature rise detection and aging life assessment of transformers under the influence of harmonic currents.

For the quantitative analysis of system strength of receiving end system, the formula of short circuit ratio about the network structure parameters is obtained by deducing the Thevenin equivalent circuit of receiving end system. The power stability of Thevenin equivalent circuit is analyzed. It is shown that the value of short-circuit ratio is related to the equivalent impedance angle, the phase angle difference between grid-connected voltage and Thevenin equivalent potential, and the critical short-circuit ratio under some limit conditions is further discussed. The phase angle difference index is proposed to evaluate system strength of receiving end system, and the standard of dividing system strength of receiving end system by the phase angle difference index is given. The relationship between the phase angle difference index and the potential phase in the generator is analyzed. A simulation example is given to verify the validity of the phase angle difference index in evaluating system strength of receiving end system.

To enhance the accuracy and reliability of photovoltaic power prediction, this paper proposes a short-term photovoltaic irradiance prediction model based on the improved grey wolf optimizer (GWO), variational mode decomposition (VMD), particle swarm optimization (PSO), and long short term memory (LSTM). First, LGWO optimizes the key decomposition parameters K and α1 of VMD to construct a parameter optimization model. Then, LSTM predicts each mode parameter decomposed by the optimized VMD. Simultaneously, the improved PSO, using root mean square error as the objective function, optimizes the neural network parameters, including the number of LSTM hidden layers, LSTM iterations, and LSTM learning rate. This ultimately improves the prediction accuracy and reliability of the model. Finally, the output modeling and prediction analysis of a photovoltaic power plant in Gansu Province are conducted. The results show that, compared with the traditional LSTM prediction model, the proposed algorithm significantly improves the prediction accuracy.

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

In order to improve the quality of power supply and the ability of stable operation of power system, the method of additional damping is usually used to suppress the harmonic resonance generated by the system. Compared with passive damping, the active damping strategy not only has well harmonic suppression effect, but also has the advantages of low loss and easy realization, so it has been widely used in recent years. However, the traditional active damping strategy mainly adopts the fixed control parameter design, which may cause the harmonic resonance problem when the working condition changes greatly. To solve this problem, based on the compound control strategy composed of harmonic compensation and resonance suppression in hybrid compensation system, an adaptive additional active damping composite control strategy is proposed in this paper. Firstly, we establish the parallel resonance equivalent model of the hybrid compensation system, and analyze the shortcomings of the traditional compound control strategy. On this basis, the harmonic voltage at PCC point is extracted by the second-order generalized integrator-frequency lock loop (SOGI-FLL), and the harmonic resonance suppression in the power supply system is designed and realized by adapting the parameters of the resonant suppression controller with the method of harmonic content limitation. Finally, the feasibility and effectiveness of the proposed method are verified by simulation experiments.

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

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

With the gradual increase of photovoltaic(PV) penetration in low-voltage distribution network(LVDN), solving the voltage overrun problem at the grid-connection point by PV inverter power control has received wide attention. Therefore, this paper proposes a voltage control strategy for grid-connected points based on power adaptive adjustment, considering the operating constraints of PV inverter and the relationship between output powers. Firstly, the PV inverter operation process is divided into two modes and six different zones, and the limit values of inverter output power in different operation intervals are clarified. Then, the power outputs of the inverter in the two modes are analysed in detail, and the voltage overruns are classified into the priority of governance in different operation zones. Finally, considering the influence of power output on grid-connected point voltage when voltage crosses the limit, the power reduction effect coefficient is defined respectively when voltage crosses the upper and lower limits, and the governance effect after active reduction is analysed, making full use of the adjustable capacity of the inverter, and reducing the PV grid-connected active reduction as much as possible.The simulation results show that the proposed control strategy can effectively suppress the voltage overrun at the PV grid-connected point.

Lithium-ion batteries are widely used in electric vehicles, energy storage systems, and consumer electronics, yet their safety and performance management remain challenging. Ultrasonic detection technology, characterized by its non-destructive and real-time capabilities, has been increasingly applied to monitor the internal states of batteries across multiple dimensions, providing novel insights into battery safety and performance management. This paper systematically reviews the applications of ultrasonic technology in battery safety and performance detection, covering critical issues such as state estimation, thermal runaway, lithium plating, polarization, and gas generation. Ultrasonic technology can sensitively capture internal temperature fluctuations, structural changes, and gas evolution within batteries, supporting early fault detection and accurate state estimation. The future application of ultrasonic detection in performance enhancement, health management, and lifecycle optimization offers significant potential, providing robust support for the innovative development of intelligent battery management systems.

This paper addresses the practical problems of slow speed and low efficiency in traditional voltage regulation in transformer areas, and proposes a multi-time-scale voltage feedback correction control and its optimization strategy aimed at the comprehensive management of voltage over-limit, fluctuation and imbalance. Firstly, a priority evaluation mechanism for adjustable resources in transformer areas based on voltage sensitivity was established. The voltage sensitivity of reactor groups, energy storage, and flexible interconnection devices was derived and analyzed. Considering multi-dimensional weight characteristics, a comprehensive evaluation of the regulation priority of adjustable resources was conducted. Based on this, for the key nodes of the transformer area, combined with the proposed multi-scenario equipment adjustment scheme, the multi-time-scale voltage feedback correction control strategy was analyzed from three modules: data input, voltage prediction and instruction output. Further considering the influence of measurement errors and unknown disturbances, based on the design of the optimal prediction window, corresponding optimization strategies are proposed.

A flyback micro-inverter with active clamp and power decoupling circuit is proposed. This circuit incorporates an auxiliary winding on the primary side of the transformer to transfer the power double-frequency pulsation to an energy storage capacitor, thereby reducing the low-frequency ripple in the input current and decreasing the capacitance of the input filter capacitor. Additionally, this energy storage capacitor serves as a clamping capacitor, absorbing the leakage inductance energy of the transformer and enabling zero-voltage switching (ZVS) of the main switch. This paper provides a detailed analysis of the circuit’s operating principle and control strategy, along with the design process for key parameters. An 80W micro-inverter prototype was built for experimentation, and the experimental results confirm the feasibility of the proposed scheme.

Traditional transformer expansion techniques commonly involve increasing the physical capacity of transformers to cope with the continuous and steady growth of electrical loads. However, this expansion method struggles to address short-term load spikes, requiring additional investment costs and often leading to idle transformer capacity. To solve these issues, this paper proposes a transformer capacity expansion scheme based on energy storage devices, leveraging the advantages of additional power support from energy storage systems and the maturity of traditional transformer control and protection technologies to provide diversified ancillary services for future smart distribution networks. Firstly, this paper proposes an annual average cost model for the collaborative configuration of transformers and energy storage systems. Subsequently, a collaborative optimal configuration model for transformers and energy storage systems is established, with the objectives of minimizing load curtailment indices and comprehensive economic cost indicators. To evaluate the effectiveness of capacity expansion, three comprehensive benefit evaluation indicators are introduced: namely, average effective utilization rate of transformers, peak shaving and valley filling capability, and comprehensive economic cost. Finally, simulation results demonstrate that this optimal configuration scheme can alleviate transformer overload levels in the overall power grid, balance short-term transformer overload costs with energy storage unit construction investment costs, and provide a reference for developing more scientific and reasonable application practices for the collaborative configuration of transformers and energy storage systems in future scenarios.

To address the challenges of weak ground fault characteristics and complex power flow directions in distribution networks with high-permeability distributed power sources, where traditional methods struggle to accurately locate fault sections, this paper proposes an active distribution network fault section localization method based on multimodal feature fusion. First, a variational modal decomposition optimized by the GOOSE algorithm is employed to decompose, denoise, and reconstruct the one-dimensional fault current signal. The gram sum field (GASF) is then used to convert the reconstructed signal into a two-dimensional image. Second, a complementary dual-channel model is constructed using ConvNeXt with an efficient channel attention (ECA) mechanism and 1D-CNN to extract deep features from both the two-dimensional image and the one-dimensional current signal. Finally, the dual-channel output feature vectors are concatenated, dynamically weighted via ECA for the fused features, and processed through Softmax to output the fault section location. Simulation experiments demonstrate that the proposed method achieves overall accuracy, precision, recall, and F1 score of 9826%, 9828%, 9827%, and 9820%, respectively, in an enhanced IEEE-33 node distribution system; Accuracy remains above 94% under extreme noise conditions. Multimodal feature fusion demonstrates significant advantages in model comparison, ablation studies, and generalization capability tests.

Aging failure of transformer bushing seals under thermal oxidation and compression is a key cause of oil leakage accidents. To explore their aging mechanism, multi-layer compression tests were conducted on NBR and FKM seals under varied temperatures and compression ratios. These tests combined measurements of mass change and compression set for life prediction, along with analyses including FTIR and SEM. Results indicate that thermal oxidation exacerbates NBR aging in multiple aspects. At low temperatures, aging is dominated by cross-linking, primarily driven by the volatilization of plasticizers and antioxidants, with macroscopic property degradation proceeding slowly. At high temperatures, the mass loss and compression set of the sealing ring increase rapidly, severe thermo-oxidative aging occurs, chain scission and crosslinking take place extensively, and the internal structure of the rubber is damaged. Compression, when coupled with thermal oxidation, significantly affects elasticity changes. Both excessively low and high compression ratios reduce molecular chain flexibility, which is detrimental to maintaining elasticity. A 25% compression ratio is optimal as it slows stress relaxation and ensures better sealing reliability. Its predicted service life at 40 ℃ reaches 1772 years, far longer than the 471 years at 20% and 553 years at 30% compression ratios. During aging, the compressed surface of the seal is less exposed to air, resulting in a lower degree of aging. FKM exhibits insignificant aging characteristics due to its superior chemical stability.

In China, the transmission voltage level is getting higher and higher, and the density of transmission lines is getting bigger and bigger. Under the background of energy green low-carbon transformation, the absolute amount of power loss of overhead ground wire cannot be underestimated. The analysis of current distribution and power loss of ground wire is the basis of the study of loss reduction. However, existing calculation methods ignore the influence of distribution parameters and spatial structure of ground wire, resulting in a low calculation accuracy, and the simulation is the main research method for related researches. To solve this problem, this paper proposes a calculation method of current distribution and power loss of the overhead ground wire with different grounding modes. The dual-ground line network model is disassembled into multiple units, and the grounding, merging, and blocking operations of Thevenin’s theorem and the indeterminate conductivity matrix are used to integrate and equate multiple spacing, simplify the left and right side models of the ith spacing, and ultimately obtain the current along the ground line, and then calculate the loss of the ground line. Through the validation, it is concluded that the accuracy of the proposed method is not less than 93.69% in calculating the current distribution of ground wire with the ground mode that double ground wires are grounded tower by tower, 95.29% in calculating the current distribution of the ground wire with the ground mode that ordinary ground line is segmental insulation and single point grounding and optical fiber composite overhead ground wire（OPGW）is grounded tower by tower, and 92.54% in calculating the power loss. This method provides a theoretical foundation for the research of power loss reduction strategy of ground wire.

The end of the stator bars in large hydro-generators represents a weak link of the unit’s insulation system. The anti-corona performance imposes limitations on the further expansion of the unit’s capacity and voltage. At present, a three-segment nonlinear anti-corona structure is commonly employed at the ends of large-capacity and high-voltage units, which can satisfy the anti-corona requirements of units under normal environmental conditions. However, in high-altitude environments, the required value of the corona inception voltage for the stator bars increases, so that a design solution with an enhanced anti-corona capability is essential to ensure the long-term safety, and stable operation of the units. In light of this, the influence law of the design parameters of the anti-corona layer on the anti-corona performance is explored, and the optimized design scheme is proposed. First, a three-dimensional parametric electromagnetic simulation model of the end of the stator bar is established. Then, simulation calculations are carried out to analyze the electric-field distribution of the anti-corona layer, and the sensitivity of various design parameters to the anti-corona performance is explored. The key influencing factors for anti-corona design are identified, and an optimized design scheme is proposed based on the genetic algorithm. The research findings indicate that parameters such as the nonlinear coefficient of the anti-corona layer, the low resistance intrinsic conductivity, the intrinsic conductivity of the nonlinear anti-corona layer, and the thickness of the anti-corona layer have a significant impact on the anti-corona performance. By using the genetic algorithm, an improved design scheme with superior anti-corona effects can be obtained. Compared with the initial scheme, the maximum electric-field strength of the anti-corona layer of the optimized scheme is reduced by 34.69%. This research provides valuable references for the anti-corona optimum design of the stator bar ends.

Since a large number of renewable units are integrated to the power system, the system inertia level is reduced, and the frequency stability of the power grid is faced with serious challenges. Therefore, a day ahead scheduling model of combined generation systems of thermal power plants and battery energy storage system (BESS) considering frequency security constraints of N-1 failure for thermal power plants is proposed. Firstly, for combined generation systems of thermal power plants and BESS, a frequency safety constraint is established for the N-1 fault of thermal units. Then, on this basis, the day-ahead scheduling model of combined generation systems of thermal power plants and BESS is established, which can rapidly increase the power by using the characteristics of the instantaneous discharge of BESS under N-1 failure of thermal power plants, and thus ensure that the system frequency can be operated in the secure range, while making the life damage percentage of the energy storage battery relatively low. Finally, this paper simulates and analyzes this proposed day ahead scheduling model in a practical case study, and verifies the effectiveness of the proposed model.

In complex and narrow environment, the stable power supply for downhole intelligent valve is one of the core technologies of intelligent stratified oil and gas production. It is difficult to solve the efficient transmission problem of wireless power transfer technology under coaxial misalignment in this application, which seriously affects the efficient and stable power supply of downhole intelligent valves. Therefore, this paper presents a series of wireless power transfer systems with stable and high efficiency under coaxial migration, which can effectively ensure the stability of the optimal load point under coaxial misalignment. Firstly, the optimal loads of four hybrid topologies are designed and analyzed, and the variation of optimal loads characteristics are summarized. Secondly, a kind of coaxial annulus Double-D (DD) coil is designed to be used in the hybrid topology under the narrow gap environment, and based on this, the coaxial misalignment characteristics of the magnetic coupler are analyzed. Then, the parameters of the hybrid topology are configured, and the parameters are designed according to the optimal load variation trend in different scenarios. Finally, a set of experimental prototype based on input-parallel-output-series type hybrid topology is built. The experimental results show that the optimal load fluctuation range of the system is only 12.6% and the average efficiency reaches 90.47% when the coaxial misalignment is 30 mm (mutual induction change 61.63%), which verifies the correctness of the theoretical analysis and it has certain engineering application value.

Renewable energy uncertainty poses serious challenges to the safe, economical and flexible operation of power systems. By integrating flexible resources, flexible resource aggregators can enhance system flexibility and promote renewable energy consumption. However, the internal adjustment ability of resource aggregators is limited, and it is difficult to deal with the complex and changeable operation needs alone. This paper uses the prediction error of renewable energy and the probability characteristics of flexible resources, quantifies the internal operational risk and flexible resource capacity of resource aggregators, and builds a flexible resource interaction model based on operational risk. A stochastic robust optimization model of active distribution network considering the flexibility and resource interaction characteristics of resource aggregators is established to improve the robustness and flexibility of the system through operational risk transmission and resource interaction among resource aggregators. The model is solved by means of target cascade method and stochastic robust algorithm based on column and constraint generation. The effectiveness of the model is verified through simulation case studies.

The gearbox of a wind turbine is the core component for energy transmission in wind turbine units. Vibration signals, serving as a critical data source for fault diagnosis of the gearbox, can accurately reflect the internal dynamic characteristics of wind turbines. However, during actual operation, internal vibration signals of gearboxes are susceptible to multi-source compound noise interference, causing traditional signal denoising techniques to face bottlenecks in effectively separating noise. There is an urgent need to develop adaptive denoising methods for wind turbine gearboxes operating in complex service environments. This paper proposes a hybrid denoising method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), genetic algorithm (GA), and wavelet threshold denoising (WTD). By constructing three-dimensional time-frequency-energy evaluation metrics under unknown ground truth conditions and establishing a parameter space mapping model, the method achieves multivariate collaborative optimization of wavelet basis functions, decomposition levels, threshold rules, and correlation coefficient thresholds. The system overcomes the limitations of single metrics through complementary validation of smoothness, spectral entropy, and residual energy ratio. Experimental results demonstrate that this method elevates the signal-to-noise ratio (SNR) to 12.16 dB in simulated signals, and achieves a root mean square error of 0.78×10-2, significantly outperforming traditional algorithms in SNR and transient feature preservation. Through the hierarchical noise elimination mechanism combining CEEMDAN modal decomposition and WTD, along with GA’s global parameter optimization capability, it breaks through the bottlenecks of traditional methods that rely on empirical settings and struggle with coupled parameter optimization. This provides a high-fidelity signal preprocessing solution for early fault diagnosis of wind turbine gearboxes, holding significant engineering implications for enhancing the reliability of wind turbine condition monitoring systems.

Long-stator linear motors typically operate with sectionalized power supply, requiring rapid power transfer between stator units during linear motion. However, frequent switching induces abrupt load variations that generate current surges, threatening the reliable operation of both the motor and the power supply system. This paper proposes a fast and smooth power switching strategy integrating time-optimal control with PI regulation. The approach establishes a mathematical model of the linear motor during switching transitions and derives time-optimal turn-off/on voltage vectors alongside their corresponding minimum switching durations through time-optimal control theory. By applying the time-optimal turn-off voltage for rapid decay of the outgoing segment’s current while combining the time-optimal turn-on voltage with a PI controller output reuse mechanism for precise current establishment in the incoming segment, the proposed strategy eliminates current overshoot while compressing the switching process to 2 electrical radians. Finally, a computer simulation model validates the proposed approach, with results conclusively demonstrating the effectiveness of the switching strategy.

To address the unclear mechanisms of energy consumption in high-speed maglev traction power supply systems under multi-parameter interactions, this study proposes an integrated qualitative-quantitative analytical method to systematically investigate the operational principles of key parameters affecting traction energy consumption. The methodology framework consists of three phases. First, a mathematical model of the traction system is established to qualitatively analyze the impact pathways and directional effects of critical parameters such as maximum speed and line gradient on energy consumption. Second, a high-speed maglev hardware-in-the-loop simulation platform is utilized to acquire multi-scenario energy consumption datasets across full parameter domains. Third, regression analysis combined with a random forest algorithm enables quantitative evaluation, transitioning from single-factor effects to multi-factor coupling importance ranking, thereby revealing the influence magnitude, weight distribution, and interaction patterns of parameters on per-capita kilometer energy consumption.Key findings indicate that maximum operating speed contributes 39.7% and line gradient accounts for 28.9% of the total impact, constituting dominant factors. Secondary influences include train formation quantity at 10.8% and passenger load factor at 11.4%, while stator segment length and step transition mode exhibit marginal effects below 5% each. The random forest model demonstrates 82.47% explanatory power for energy consumption variations, providing a theoretical foundation for energy efficiency optimization in maglev traction systems.

The thyristor valve inverter is the core equipment in current ultra-high voltage and extra-high voltage direct current transmission projects. Identifying the online health status parameters of the thyristor level in the valve inverter is of significant importance for ensuring the safe operation of the valve inverter and improving equipment availability. This paper takes the Six-pulse inverter as the analysis object. Through the analysis of the working process of the Six-pulse inverter circuit and combining specific circuit topology and monitoring information of thyristors in the valve inverter in practical engineering, a digital twin model of the Six-Pulse Inverter is constructed. The simulation results of the Six-pulse inverter in Matlab/Simulink are used to simulate the monitoring data of the actual physical model. Considering the conditions of multi-level thyristors in series, the circuit equations are solved using the Runge-Kutta method to obtain digital model data. Furthermore, based on the particle swarm algorithm, the equivalent insulation resistance and damping capacitance parameters of the thyristor level are identified. The results are analyzed in conjunction with trajectory sensitivity analysis, confirming that the proposed method has good identification accuracy. It provides valuable references for intelligent operation and maintenance of the valve inverter.

Power prediction is a key technology for large-scale safe and reliable application of wind energy in the power system, and the pure data-driven methods currently mainly used have shortcomings such as poor interpretability and large deviation. To effectively predict the power of wind turbines, a surrogate modeling method is proposed for the aerodynamic characteristics of wind turbine blades, which are the core components of wind turbine. Based on the blade element momentum theory, each blade is divided into dozens of elements, and the turbulent fluid field of the blade is analyzed element by element under different wind speeds and angles of attack to obtain the corresponding dynamic characteristics, such as lift and/or drag force. The Kriging method is used to construct the surrogate model of blade elements, and then the overall dynamic characteristics of the surrogate model is obtained by element integrating along the span direction of the blade. The whole process of surrogate model construction and utilization is given in detail in this paper. The proposed method is a fusion of both the physical model and the data-driven model, which can be used as a system-level rapid analysis model to provide support for unit-level and wind farm-level power prediction in scenarios such as wind turbine design and running.

To achieve the “Dual Carbon” goals at an early stage, the penetration of renewable energy in power systems continues to increase, while its inherent volatility imposes higher requirements on the flexible accommodation capability of thermal power. Under fast load variation conditions, integrating thermal power with molten salt thermal energy storage (TES) retrofits so as to form a coupled system can enhance renewable energy accommodation capacity. However, its carbon-economic performance must be thoroughly quantified. To fully exploit the flexibility of thermal power while ensuring carbon-economic benefits, this paper proposes a carbon-economic optimal dispatch model for a coupled thermal power and molten salt TES system considering fast load variation. First, to control coal consumption and carbon emissions after flexibility retrofits for fast load variation and rapidly mitigate renewable energy fluctuations, an operational model of the coupled system integrating thermal power with molten salt TES retrofits is constructed. Second, a carbon emission allowance trading model for thermal power is established, linearizing the nonlinear peak-shaving correction coefficients in carbon emission allowances to quantify the dynamic carbon trading costs across time intervals. Furthermore, by incorporating carbon trading costs into the economic dispatch model of the coupled system, a carbon-economic optimization model coordinating the retrofitted coupled system with renewable energy units is solved to maximize daily comprehensive operational revenue. Finally, case studies based on a real-world power grid in Jiangsu Province demonstrate that the proposed model can effectively enhance system economy while achieving carbon emission reduction.