Electromagnetic drive forming technology is a special forming process that uses pulsed Lorentz force to drive a high-conductivity sheet to move, thereby driving a low-conductivity sheet to cause plastic deformation, which can effectively make up for the shortage of traditional electromagnetic forming in forming low-conductivity materials. However, in the existing electromagnetic drive forming, the driver sheet also undergoes plastic deformation, which tends to lead to a serious problem of wastage of the driver sheet, and it is difficult to regulate the forming shape.

To solve this, instead of the traditional circular drive sheet, a solid copper ring with a specific thickness is employed, utilizing the strong electromagnetic force generated in the copper ring to propel it at high speed into collision with a metal sheet. This impact generates a contact force, causing the sheet to undergo plastic deformation. Additionally, an electromagnetic-structural coupling model for the copper ring electromagnetic drive forming process is developed using LS-DYNA software. A series of electromagnetic drive forming experiments are then conducted, using a TA2 titanium plate as the test material, to validate the feasibility of the proposed method. Numerical simulation and experimental results show that under a single discharge (7 kV), a metallic copper ring with a diameter of 80 mm can drive the titanium plate to deform and the forming height can reach 14 mm. Meanwhile, based on strain analysis of the forming sheet and the driven ring, the solid copper ring does not deform and can be reused. In addition, by changing the size and shape of the copper ring, the forming profile of the plate can be flexibly adjusted. For example, when the diameters of the circular driving rings are 65, 80, and 95 mm, uniformly deformed areas with diameters of 58, 72, and 87 mm are observed on the top of the sheet, which is highly consistent with the shape of the rings. Even if the forming height is increased, the forming shape of the center area of the sheet remains a flat-topped profile when enhancing the discharge voltages. On this basis, the dynamic deformation process of the sheet is further investigated through numerical methods, to reveal the deformation behavior and forming mechanism of the titanium plate driven by the copper ring, which demonstrates that the forming velocity approaching 100 m/s and the strain rate is up to 1 000 s^-1. Hence, this forming process belongs to the category of high-speed forming technology.

The obtained results indicate that, since the copper ring is a solid ring with a specific thickness, it does not experience plastic deformation during the electromagnetic drive forming process and can be reused. This effectively addresses the issue of excessive waste of the driver sheet in conventional electromagnetic drive forming. The copper ring also provides shape adjustment capabilities, allowing for the formation of sheets with circular, quadrilateral, and hexagonal flat tops. The height of the flat-topped profile can be controlled by adjusting the discharge voltage, overcoming the problem of limited shape flexibility in existing electromagnetic drive forming methods. These results are of significant practical value for advancing and expanding the applications of electromagnetic drive forming technology.

The flat open-circuit voltage versus state of charge (SOC) curve of LiFePO₄ batteries leads to difficulties in achieving an accurate estimation of SOC using only electrical signals. In addition, there are limitations in SOC estimation methods for single electrical, thermal, and acoustic data sources. In view of this, a multi-source data feature extraction method for SOC estimation of LiFePO₄ battery was proposed. A comprehensive feature extraction was carried out on the electro-thermal-acoustic multi-source data obtained from different angles. Considering the advantages of different feature selection methods, a new feature selection method integrating Spearman correlation coefficient, mutual information, category boosting and least absolute shrinkage and selection operator regression was proposed. The joint selection of electro-thermal-acoustic key features was realized to improve the accuracy of SOC estimation.

Firstly, an experimental platform for LiFePO₄ batteries was built. Electro-thermal-acoustic multi-source data were acquired. The transient features and short-term variation features of electrical and thermal signals, as well as the time-domain, frequency-domain, and time-frequency-domain features of ultrasonic signals were extracted, respectively. Secondly, in order to select the key features more accurately, a new method of feature selection incorporating Spearman correlation coefficient, mutual information, category boosting, and least absolute shrinkage and selection operator regression was proposed. In order to verify the performance of the proposed method, the proposed method was compared with SOC estimation results using all features and SOC estimation results under different feature selection methods. The effect of SOC estimation using single data source features versus multi-source data features was compared. The feasibility of the proposed method was verified at different magnifications and under different operating conditions. Finally, Gaussian white noise with different signal-to-noise ratios was added to the raw ultrasound signals acquired under dynamic stress test (DST) conditions and new european driving cycle (NEPC) conditions, respectively, to verify the applicability of the proposed method under high-intensity noise.

The results show that using the new method of feature selection proposed can effectively select the features that are important for SOC estimation with higher accuracy than SOC estimation using all features. With the same number of features, the SOC estimation accuracy of this method is improved compared with that of a single feature selection method. The model constructed using electric-thermal-acoustic multi-source key features has higher SOC estimation accuracy compared to single data source features. When using the BiGRU model, the mean absolute error and root-mean-square error of SOC estimates are 0.58% and 0.72%, respectively. The method performs well under a single operating condition. The method also shows good applicability at different discharge multipliers and under multiple operating conditions. Under DST conditions and NEDC conditions, the mean absolute error of SOC estimation is 0.91% and 0.98%, and the root mean square error is 1.03% and 1.13%, respectively, which verifies the validity and accuracy of the method. After adding noise with different signal-to-noise ratios to the original signals of different working conditions, the wavelet noise reduction can resist the noise interference in the actual environment to a certain extent and maintain the accuracy of SOC estimation.

With the rapid development of the global economy, offshore wind power generation technology has been advancing towards field group scale and industrialization, becoming a research hotspot in international renewable energy. However, to reduce the economic costs associated with deep-sea wind power technology and enhance the efficient of wind energy capture and utilization, the capacity of wind turbines has been gradually upgraded to 10 MW and above. This trend towards large capacity has consequently led to increased weight and volume of wind turbines, complicating offshore transportation, lifting, operation and maintenance, which limits further development of offshore wind power technology. Moreover, the significant volatility and intermittency of offshore wind power contribute to increased grid penetration issues, difficulties in large-scale grid connections, and a notable phenomenon of wind curtailment. Furthermore, the non-stationary wind power can cause grid voltage fluctuations, flicker, frequency fluctuations, harmonics and other power quality problems, affecting the stable operation of the grid.

To address these problems, Hunan University's wind power generation team proposed an innovative integrated technology for hydrogen production through offshore superconducting wind power generation. This innovative system utilizes water electrolysis to locally consume offshore wind energy, with the produced liquid hydrogen being transported to land via ships or pipelines for comprehensive utilization. Additionally, a liquid hydrogen circulation refrigeration system provides a stable low-temperature environment for superconducting wind turbines, significantly reducing platform volume and weight and ensuring the reliable operation of the integrated system.

The article provides an overview of recent development in HTS wind turbine technology and offshore wind power hydrogen production technology, both domestically and internationally. It analyzes the key structures and feasibility of the proposed innovative integrated system, highlighting how it compares to traditional technologies. Additionally, the article explores recent advancements in offshore wind power generation and transmission technologies. The discussion then shifts to the benefits of the proposed innovative technology in comparison to other existing technologies and schemes. It summarizes the advantages of integrating hydrogen production and offshore superconducting wind power generation, analyzes the variability of superconducting wind turbines output power and the limitations of current converter topology control strategies, and proposes the key technologies of designing superconducting wind turbines converter topology with efficient energy transfer capability and designing a superconducting wind power system friendly control strategy.

For the future development of the integrated system, an energy island system plan that is integrated with renewable energy development is proposed. This plan is based on the operational principles of each sub-structure and aims to harness the efficient synergy of renewable energies. Research will focus on determining the appropriate ratios for various energy production and conversion devices, which will optimize the configuration of multi-energy complementarity. This approach aims to establish an integrated energy system that reduces the standby capacity required by the system’s various equipment. Furthermore, this initiative will promote the coupling of the power with renewable energy systems, facilitating the synergistic development of electric power and green hydrogen. This strategy will improve the optimized configuration of the energy supply system and establish a common technological framework for large-scale superconducting wind power hydrogen production technology.

When multiple EMUs are simultaneously in a light-load starting condition within the traction network, it can lead to low-frequency oscillations in the traction network voltage. In severe cases, this may trigger traction locking, which poses a risk to the operational safety of high-speed trains. To address the issue of low-frequency oscillation, this study proposes a method based on virtual impedance for its suppression.

First, the impedance model of the EMUs-traction network coupling system is derived, and its stability is analyzed using the impedance ratio stability criterion and the Bode diagram. Second, based on the stability criterion, virtual impedance is incorporated into the EMU control strategy to correct the impedance characteristics of the load subsystem, thereby proposing a low-frequency oscillation suppression method. Third, an adaptive control method for virtual impedance is designed to handle the complex and dynamic working conditions, enabling the system to adjust the virtual impedance parameters and enhance the effectiveness of the suppression method. Finally, the proposed control strategy is compared with the traditional approach through a low-power experimental platform, validating the effectiveness of the suppression method.

The results of the system stability analysis indicate that the logarithmic amplitude-frequency characteristic curve of the system impedance ratio is significantly lower than 0 dB when only one unit (m=1) is connected to the traction network, and the absolute value of the system impedance ratio |T_sn(s)| is much less than 1, suggesting system stability. As the value of m increases, the amplitude-frequency characteristic curve approaches 0 dB, resulting in a decline in system stability. When m=6, the amplitude-frequency characteristic curve crosses 0 dB at 7.032 Hz, and the phase angle at the crossover frequency (f_c) is 182°. The absolute value of the phase angle exceeds 180°, and the system impedance ratio |T_sn(s)| does not meet the condition of being significantly less than 1, indicating an unstable state. Simulation results with the virtual impedance control strategy indicate that when six EMUs are connected to the traction network at the same time, when the virtual impedance R_p values are 10 Ω, 5 Ω, 2 Ω and 1 Ω respectively, the system voltage and current reach a steady state at 1.6 s, 1 s, 0.3 s, and 0.2 s, respectively. The suppression effect improves as the virtual impedance decreases. The simulation results with the adaptive virtual impedance control strategy show that when six EMUs are connected to the traction network, the system reaches a steady state in approximately 0.5 s. The voltage regulation time for the DC side of the EMUs is 0.46 s, with an overshoot of 37.71% and a post-stabilization voltage fluctuation of 58 V. Subsequently, one EMU with adaptive virtual impedance control is added every 2 seconds, resulting in a total of 11 EMUs. The system remains stable, and the virtual impedance value is reduced to 4.61. The comparative experimental results indicate that, after transitioning from the traditional control strategy to the adaptive virtual impedance control strategy, the system can rapidly recover from a low-frequency oscillation at approximately 7 Hz. Furthermore, the AC-side voltage stabilizes at 20 V, while the DC-side voltage remains stable at 40 V.

Based on the analysis, the following conclusions can be drawn: (1) A higher impedance ratio between the two sides of the EMUs-traction network coupling system results in decreased system stability. When the impedance ratio does not meet the stability criterion, system instability is induced, leading to low-frequency oscillations. (2) The incorporation of parallel virtual impedance in the control strategy for the EMU's four-quadrant converter can correct the impedance characteristics of the load subsystem, enhance system stability, and suppress low-frequency oscillation. (3) The adaptive virtual impedance control method can autonomously adjust the virtual impedance value based on the intensity of voltage oscillations on the network side of the EMU, thereby suppressing low-frequency oscillations under varying load conditions, improving the adaptability of the control strategy, and ensuring the stable operation of the EMUs-traction network coupling system across different operating conditions.

As the penetration rate of renewable energy sources increases, the stability mechanisms of the power system are constantly changing. The double-fed induction generator (DFIG), as a mainstream renewable energy equipment, its stability is of great significance to the safe operation of the power system. The phase-locked loop (PLL) plays an important role in synchronization, but there has been less research on simultaneously considering the dynamics of the phase-locked loop and the power balance loop. Moreover, the small-signal synchronization mechanism of DFIG within rotor speed timescale needs to be further analyzed.

Firstly, the transient model of single-DFIG infinite-bus system is constructed within the rotor speed scale. The simplified model is compared with the full-order model by Matlab/Simulink, and the results show that they match very well. Then, through bifurcation analysis, it is found that the system would experience small disturbance instability under weak grid condition. And it manifests itself in the form of low-frequency oscillatory instability. Furthermore, through the dominant modal analysis, it is found that the dominant unstable loop is the power balance loop.

In order to analyze the small-signal synchronous instability mechanism of the system, the model is linearized around the operating point. The linearized model and the full-order model are compared using Matlab under small disturbance, and the results validate the rationality of linearization. The power balance loop dominates the instability, making it considered as the core loop. Therefore, the Heffron-Philips model of the system is established for analyzing the small-signal synchronous stability mechanism. Based on the complex torque coefficient method, the terminal voltage control loop plays a dominant role by introducing negative damping. And by studying the transfer function of the PLL, it is found that the PLL with typical parameters has a negligible impact on the system in the rotor speed scale, and can be approximately regarded as a constant.

Finally, the parameters of the active outer loop and the reactive outer loop are analyzed. With the changing of the grid strength, the damping torque and synchronizing torque of each branch are quantitatively calculated. It is found that increasing the proportional coefficient of active outer loop and decreasing the integral coefficient will improve the stability of the system, and increasing the proportional/integral coefficient of the terminal voltage control loop will benefit the stability of the system. These analyses have been verified through simulations and experiments.

The conclusions of this paper are as follows: (1) In the rotor speed scale, the small-signal synchronous instability of the single-DFIG infinite-bus system is dominated by the power balance loop (active outer loop and rotor dynamic), rather than the PLL. (2) By constructing the Heffron-Philips model, it is found that the synchronous phase ∆θ_pllis approximately represented by an algebraic expression of the state variable ∆ω_r/∆θ_rof the rotor. The essence of synchronous instability lies in the instability caused by the state variable of the energy storage element.3) Using complex torque coefficient method, it is found that the terminal voltage control loop is the main factor that introduces negative damping. Through the analysis of the influence of parameters, it is found that increasing the proportional coefficient of the active power outer loop and decreasing the integral coefficient will improve system stability, and increasing the proportional/integral coefficient of the terminal voltage control loop will be beneficial to system stability.

PV systems are typically equipped with reactive power compensation devices when connected to the grid, and static synchronous compensator (STATCOM) devices are widely employed due to their flexible control capabilities. The increasing utilization of power electronic devices in the power grid has resulted in a shift from physical synchronization to control synchronization as the dominant mode of system operation. Analyzing static synchronization stability problem is more challenging for these systems compared to conventional power systems, as converter output characteristics are influenced by control strategies. Therefore, it is imperative to urgently address the problem of static synchronization stabilization under control strategy dominance.

First, this paper establishes the static synchronous stability analysis model of the grid-connected converter based on the control loop and circuit structure of each converter in a parallel system under the respective dq reference frame. The dq reference frame of the converter is determined by the phase information provided by the control loop in a multiple converter parallel system, thus enabling a unified coordinate system for static synchronization stability analysis. Subsequently, an equivalent small signal model is developed for analyzing multiple grid-connected converters. In comparison with existing coordinate conversion methods, Kirchhoff's current law is incorporated to enhance accuracy and reduce errors.

Then, the stability criterion for impedance analysis is enhance, and the static grid-synchronization performance indices are created. The small perturbation oscillation characteristics are measured using overshooting and regulation time, while the participation factor is employed to analyze the impact of each pole of the system. Finally, the attenuation coefficient is utilized to assess static synchronization stability performance.

The model is developed in Matlab/Simulink for simulation verification. Subsequently, an analysis is conducted on the impact of parameters such as the control loop parameters and STATCOM capacity on static synchronization stability. The main conclusions are summarized as follows:

(1) Optimizing the reactive power output of the grid-connected converter based on known control parameters can significantly enhance static synchronization stability performance, with the dominant influence of small perturbations after oscillation mode being attributed to poles generated by phase-locked loop control.

(2) The attenuation coefficient of the system exhibits a rapid increase in proximity to the critical stability region. Hence, it is imperative for the system to possess a certain margin of attenuation coefficient during operation. Based on the simulation analysis results presented in this study, static synchronous instability phenomena occur when the attenuation coefficient of the PV system exceeds 300. Conversely, when the attenuation coefficient falls below 250, the system remains in a state of static synchronous stability. These findings establish a criterion for analyzing and assessing static synchronization stability within such systems.

(3) The addition of STATCOM to the PV system primarily impacts the conductance matrix transfer function of the q-coupled channel. The phase-locked-loop coupling oscillations between the grid-connected converters do not affect the dd channel. Within the stable operating region, an increase in bandwidth for the DC voltage control loop, active current control loop, and reactive current control loop results in an amplification of both attenuation coefficient and system oscillation amplitude.

(4) When the phase-locked loop parameters of the STATCOM are the same as the PV system, the stability performance of the system is mainly affected by the grid impedance and the equivalent conductance transfer function of each grid-connected converter. And each grid-connected converter can independently connect to the grid and achieve stable operation to ensure that the system achieves static synchronous stability in this case.

(5) In cases where the active output of the PV system is low, STATCOM typically adjusts its capacitive or inductive reactive power provision to improve static synchronization stability performance. Conversely, when compensating for capacitive reactive power, utilizing STATCOM may yield superior results compared to using the PV grid-connected converter alone. Hence, allocating an optimal capacity for STATCOM can significantly enhance static synchronization stability performance.

As the penetration rate of renewable energy resources continues to increase, the traditional power system based on synchronous generators is evolving into a power system based on diversified power electronic equipment. The small disturbance stability analysis problem of multi-converter grid-connected system has attracted widespread attention. State-space model and impedance-based model are two main small disturbance stability analysis methods. Being as the white-box method, state-space model can be difficult to apply in practice because the differential equations describing the controllers of converters are not generally openly available due to commercial confidentiality. Impedance models have been popular in the field of power electronics for analysis of interactions between grid and converters. However, applying it directly to the stability analysis of multi-converter system will make the analysis process very complicated. Generally, the existing state-space and the impedance method still have room for improvement for the small disturbance stability analysis and sensitivity analysis of the oscillation mode of each converter.

Firstly, this paper proposes a single-input single-output (SISO) dq impedance stability criterion for analyzing the small disturbance stability of the multi-converter grid-connected system. Secondly, based on the formula of the stability criterion proposed, an expression for calculating the closed-loop pole of the system is derived. Because this formula is only a scalar function, the accuracy can be guaranteed for the usage of vector fitting (VF) method. Furthermore, a method for analyzing the sensitivity of oscillation modes to the impedance/admittance of each converter is proposed. This method can effectively evaluate the influence of different converters on the oscillation modes and help identify the dominant converter that causes oscillations. Finally, the accuracy of the proposed method is verified by Matlab/Simulink simulation and hardware-in-the-loop experiment.

The results are as follows: firstly, the proposed multi-converter system model can be used to represent the converter grid-connected system with any network structure and any number of grid-forming and grid-following converters. Based on the proposed method, it can be used to analyze the overall stability of the system as well as the influence of each converter on the system stability. Secondly, the proposed sensitivity analysis method can be used for evaluating which power converters are more sensitive to the close-loop poles and have a significant contribution to the harmonic instability.

The following conclusions can be drawn from the above results: (1) A recursive stability evaluation method for analyzing the stability of the multi-converter grid-connected system based on SISO dq impedance ratio is achieved, and a complete stability evaluation procedure is provided. Compared with the stability analysis method based on generalized Nyquist criterion, the stability analysis problem of a MIMO system is transformed into the stability analysis of a series of SISO systems, and the stability analysis of the whole system can be realized only by the impedance ratio of d-axis and q-axis in the stability analysis process. Because the SISO impedance ratio is used for stability analysis, the solution of the eigenvalues of the high-order return rate matrix required by the traditional method can be avoided, and the complexity of Nyquist plot analysis required for MIMO system can be effectively reduced. (2) In the proposed impedance stability criterion, dq impedance is adopted to model the VCI while dq admittance is used to describe the CCI, so the complicated procedure for obtaining the RHP open loop poles can be avoided. (3) Based on the proposed stability criterion proposed, an expression for calculating the closed-loop pole of the system is derived. Since this paper adopts dq coordinate system for modeling and derivation, compared with the sequential impedance model, the transfer function matrix elements can be guaranteed to be rational fractions, so the closed-loop poles can be obtained by VF method. (4) The sensitivity formula of the system's closed-loop poles on the dq admittance/impedance of the converter in the system is derived in this paper. Combining with the residual of the closed-loop poles obtained by the VF method, it can be used to analyze the influence of each converter on the key modes in the system. Therefore, it is helpful to identify the source that causes oscillatory instability.

The increasing deployment of wind turbines in challenging environments has led to the prevalent issue of converter faults, which significantly affect the reliability and efficiency of wind power systems. Given the critical role that the converter plays in optimizing the wind power conversion process, detecting and identifying open-circuit fault in wind converter is essential for maintaining operational integrity and maximizing energy output. Current fault identification methods often suffer from limitations related to robustness and computational complexity, necessitating improved solutions. To address these shortcomings, this paper introduces an innovative fault identification method that integrates analysis of the direct current (DC) bus voltage and rotor current characteristics. It can accurately recognize the single and double tube faults of converter power tubes.

Firstly，Fault detection is facilitated by the fact that the DC bus voltage signal is easily accessible, independent of the load and control strategy. Extraction of DC bus voltage edge gradients using mathematical morphology as a feature to detect the occurrence of faults. Secondly, the Pearson correlation coefficients of the rotor side currents are calculated to analyze waveform characteristics. The coupling relationship between the three-phase currents is theoretically deduced, and it is proved that the Pearson correlation coefficients between the two-phase currents are significantly different under different fault conditions, enabling precise identification of the fault phase. Moreover, the location of the fault bridge arm is determined using the average value of the current, enhancing the accuracy of fault identification. Finally, the decision function is used to locate the faulty power tube and realize the fault classification.

Simulation results of the open-circuit fault model of doubly-fed wind power converter show that the proposed method in this paper can accurately determine the occurrence of faults and locate the position of power tubes. By comparing under large data sets, it is found that the proposed method improves the accuracy while maintaining a shorter detection time compared to other methods, which is more practical and reliable. The simulation results show that the wind speed fluctuation has a negligible effect on the DC bus voltage and rotor current, and no fault occurrence is detected, while the current characteristics are stabilized in the range of the fault-free case, which indicates that the proposed method can overcome the interference of wind speed fluctuation. By simulating voltage dips to model the load fluctuations, it is found that the fault detection module misjudges the occurrence of faults, and the current characteristics is small affected but similar in size. It is judged that no faults have occurred, so the fault identification module can be used as a verification of fault detection. A Gaussian white noise with a signal-to-noise ratio of 20 dB is also added to the acquired voltage and current data, and the results show that the proposed method is not disturbed by noise.

The following conclusions can be drawn from the simulation analysis: (1) Compared with existing methods, the method is not only simple and effective in calculation, but also has a higher accuracy rate. (2) The fault detection method based on mathematical morphology utilizes the DC bus voltage, which is easy to obtain data and rapid to detect, and is not affected by noise. (3) The Pearson correlation coefficient-based fault classification method classifies the rotor three-phase currents according to their waveform correlation, and the consistency of theoretical and simulation results shows that the method is effective and of practical significance, and the method has strong robustness.

To monitor the dynamic characteristics of the receiving AC system and assess the risk of wideband oscillation in LCC-HVDC without additional equipment, this paper presents a non-invasive impedance wideband measurement method for LCC-HVDC systems. Unlike existing invasive methods, such as single voltage/current or wideband harmonic disturbance injections, the proposed method does not inject harmonic disturbances into LCC-HVDC, thereby avoiding the potential resonance risk. Furthermore, the proposed method introduces curve similarity and a dynamically adjusted time-step sampling scheme. First, this paper discusses the interaction principles between the LCC system and the receiving AC system. Secondly, this paper establishes the harmonic state space (HSS) impedance model for LCC-HVDC, based on HSS theory, focusing on the unipolar earth loop topology. Subsequently, a non-invasive impedance wideband measurement method is proposed to observe the operating state of the system, judge the risk of wideband oscillation, and analyze the interaction and wideband oscillation characteristics between the LCC system and the receiver network through impedance analysis. Finally, an example of CIGRE standard model is used to verify the correctness and practicality of the proposed method.

Based on the HSS theory and the concept of digital modulus, this paper introduces a non-invasive impedance wideband measurement method. Firstly, the system topology and the interaction principles of the LCC-HVDC unipolar earth loop subsystems are detailed. Then, the impedance modeling of the LCC-HVDC system is elaborated, and the HSS impedance mathematical model for wideband measurement is derived based on the system's topology. Finally, the system model is constructed in MATLAB/Simulink using parameters from the CIGRE standard model, and the impedance calculation model is applied to measure the impedance of the LCC system. Combined with the measured wideband impedance curve, the interaction between the LCC system and the receiving network, as well as the wideband oscillation characteristics, were analyzed using the generalized Nyquist criterion for stability. At the same time, the Pearson correlation coefficient was introduced to capture the similarity of impedance curves with high granularity, thereby exploring the adaptability of impedance models of different orders and the factors influencing the oscillation risk. It offers theoretical support for the measurement methods used in the engineering application of impedance measurement, and appropriately expanding the permissible error range in the amplitude-frequency intersection can enhance the universality of impedance analysis methods.

The main contributions and conclusions of this paper are summarized as follows: (1) A non-invasive LCC-HVDC impedance model and impedance wideband measurement method are designed, which does not require injecting harmonic disturbances into the system. The Pearson correlation coefficient between the measured impedance curve of the 13th-order model and the fitted curve from active measurements is 0.998 3, which is very close to 1. This high value indicates that the accuracy of the measurement model is high, suggesting a close match with the actual impedance characteristics. (2) By analyzing the similarity matrix of measurement curves from models of varying orders, it is observed that the Pearson correlation coefficients for the phase measurement curves are closely matched for orders h=3, h=7, and h=11, with the highest deviation being within 0.2%. Consequently, the acceptable error margin for amplitude-frequency intersection points may be suitably broadened. Considering computational efficiency, a lower-order measurement model within the range of 3 to 13 can be selected. (3) The characteristics of the measured wideband impedance curve of the system can be observed under two conditions: adaptive adjustment of the sampling time step and variation in transmission power. Adaptive adjustment of the time step is not only conducive to accurately identifying the crossover points of the amplitude-frequency response but also reduces the computational load and minimizes storage requirements. Conversely, an increase in transmission power raises the risk of system instability.

Current electric shock detection methods are primarily designed to address faults between the live wire and the ground wire, mainly relying on monitoring changes in residual current to identify issues. However, in the case of a neutral-to-live electric shock fault, the fault circuit often does not cause a significant change in the residual current. This presents a considerable challenge for existing detection methods when it comes to identifying neutral-to-live electric shock incidents.

To address the aforementioned issues, a low-voltage neutral-to-live electric shock faults detection method based on dynamic fault characteristics and a light gradient boosting machine has been proposed. Firstly, a 1:1 prototype experimental platform for a low-voltage distribution network was established in a real system. Under various operating scenarios involving multiple household loads, experiments reproducing live neutral shock faults were conducted alongside control experiments using a sliding resistor to replace the electrically shocked body. A substantial amount of experimental samples representing both fault and normal operating states was collected, creating a comprehensive database. Secondly, the complexity of neutral-to-live electric shock faults is assessed based on the interference of load current on fault current. A fault circuit electrical equivalent model is established by considering the dynamic resistance and breakdown arcs at the dual contact points of the neutral-to-live shock, in conjunction with biological dynamic impedance. The impact of fault current on the main circuit current is analyzed. Finally, features of the main circuit current are extracted from the perspective of magnitude and high-frequency components, and the temporal changes of individual features before and after the occurrence of faults are compared. Given the difficulty in clearly distinguishing between fault and non-fault states based on individual features alone, along with the fact that these features exhibit varying sensitivity to both states, a multidimensional representation of the system state is employed. Following an ensemble computational approach, a lightweight gradient boosting machine model is developed, leveraging its uni-directional gradient sampling method and ensemble operation mechanism to accurately classify the two states.

The proposed method was evaluated on a test dataset consisting of 50 666 samples, achieving an overall accuracy of 96.82%. Specifically, the identification accuracy for 35 831 normal samples was 97.50%, while the accuracy for 14 835 neutral-to-live electric shock faults was 95.17%. The test results indicated that the proposed method could accurately distinguish neutral-to-live electric shock faults from normal operating conditions, including those in the control group with the sliding rheostat added, even when the fault information was significantly obscured by high load currents. Compared to existing methods, the proposed approach shows an advantage in accurately detecting low-voltage neutral-to-live electric shock faults.

The following conclusions can be drawn from the analysis: (1) By incorporating the time-varying impedance of biological tissues, variations in contact resistance, and breakdown arcs, the dynamic characteristics of faults were examined, revealing two effects of neutral-to-live electric shock faults on the main circuit current: changes in current magnitude and variations in high-frequency components. These findings served as the basis for constructing feature vectors. (2) The contribution of individual features to distinguishing between neutral-to-live electric shock faults and normal operating conditions is limited, resulting in significant inter-class ambiguity that can easily disrupt the sample fitting performance of traditional pattern recognition models. However, if features can exhibit a certain degree of sensitivity across different classes, the combination of multidimensional features can facilitate comprehensive discrimination. (3) Due to its inherent resilience to disturbances, the ensemble model can effectively mitigate interference caused by inter-class ambiguity and demonstrate strong generalization capabilities.

Hydrogen energy system, with its inter temporal and spatial transfer characteristics, shows great potential for enhancing the resilience of distribution grids. However, few literatures have considered the inter temporal and spatial flexibility of hydrogen energy system and the inter-regional support capability of mobile emergency resources, and the post-disaster collaborative recovery mechanism of multi-region electric-hydrogen integrated energy system (MR-EH-IES) is still unclear, which makes it difficult to exploit the inter-regional support potential of mobile resilience resources. Aiming at the above problems, this paper proposes a post-disaster recovery strategy for MR-EH-IES with cross-regional resource sharing.

This paper firstly proposes a two-layer MR-EH-IES disaster recovery framework based on the idea of “intra-regional autonomy, resource integration, inter-regional sharing”. In the lower layer, the electric-hydrogen integrated energy system (EH-IES) carries out intra-zone autonomy. The potential of synergistic cooperation between mobile electric energy storage, hydrogen fuel power generation vehicles, maintenance personnel and hydrogen energy system in disaster recovery is fully considered, and the EH-IES disaster recovery model considering the synergistic scheduling of distributed power sources and maintenance personnel is established. At the upper level, the joint disaster resilience center carries out the coordinated allocation of mobile resilience resource (MRR). Starting from the disaster recovery mechanism of different types of MRR, the key factors affecting its allocation are analyzed, the MRR disaster recovery mechanism considering cross-region support is proposed, and the MRR disaster allocation model considering cross-region resource sharing is established. Then, based on the above framework and strategy, the MR-EH-IES two-layer disaster recovery model considering cross-region resource sharing is proposed.

The simulation analysis shows that the total cut-load loss of MR-EH-IES decreases by 22.4% after considering cross-region resource sharing, in which the cut-load loss of region 1 and region 3 increases slightly by ¥1.3×10³ and ¥7.3×10³, respectively, while the cut-load loss of region 2 and region 4 decreases by ¥157.8×10³ and ¥62.5×10³, respectively. Specifically, in the early stage of disaster recovery, when the mobile power supply left from region 1 and region 3 to support region 2 and region 4, the weighted load recovery rate of region 1 and region 3 showed a short drop, with the maximum drop of 0.5% and 1.1%, respectively, but both of them were higher than the weighted proportion of important loads. Meanwhile, the load-weighted recovery rates of region 2 and region 4 increased significantly, with maximum enhancements of 11.0% and 4.2%, respectively. In addition, when region 1 and region 3 were restored, idle mobile power supplies and maintenance personnel were the first to support other regions.

The following conclusions can be drawn from the simulation analysis: (1) The post-disaster recovery strategy proposed in this paper is able to rapidly restore the supply of important loads and reduce the system damage in the early stage of disaster recovery through the reasonable allocation of mobile emergency resources, and improve the utilization rate of mobile emergency resources in the later stage of disaster recovery. (2) The inter temporal and spatial flexibility of the hydrogen system and the long tube trailer can increase the energy transfer channels of MR-EH-IES in time and space scales, giving full play to the ability of hydrogen energy system to support the power grid in disaster recovery.

With the widespread access of renewable energy, the access scale of distribution network service data acquisition devices and data acquisition frequency have surged. The distribution network acquisition services are rapidly developing towards high-frequency, massive, and computationally intensive directions. It is significant to fully utilize the potential of cloud-edge-end collaboration to enhance the service carrying capacity of the network. Recently, service processing methods based on cloud-edge-end collaboration have been proposed. However, these methods still face several challenges. First, the coupling of long-term constraint guarantees and short-term processing decision optimization makes it difficult for single-slot short-term decisions to achieve long-term constraint coordination. Second, the differentiated performance requirements of services and limited network resources lead to interdependence among multi-device processing decisions. Existing methods lack a collaborative processing mechanism, making it challenging to resolve decision conflicts caused by competition. Finally, most current methods adopt random sampling mechanisms, overlooking the differences among samples in the action experience pool, resulting in poor convergence and optimization performance in resolving competition conflicts under resource-constrained scenarios. To address these challenges, this paper proposes a cloud-edge-end collaborative service processing mechanism for high-frequency data acquisition in distribution network.

Firstly, a cloud-edge-end multi-level collaborative service processing framework for high-frequency acquisition in the distribution network is designed. It constructs differentiated models for local computing, edge processing, and cloud processing to meet the varied computing requirements of data acquisition services. Further, under the premise of ensuring queuing delay and long-term average data collection constraints, the objective of maximizing the amount of cloud-edge-end collaborative processed data is set, which ensures sufficient underlying data support for the normal operation of new power services while reducing queuing delay.

Subsequently, the concept of virtual queues from Lyapunov optimization theory is introduced to transform the original problem into an online optimization problem that only depends on current slot information. It plays an important role in achieving the coordinated guarantee of delay and throughput.

Then, an improved deep Q-network based cloud-edge-end collaborative processing algorithm for distribution network is proposed, which includes five stages of initialization, action selection, conflict resolution, learning, and updating. Specifically, in the action selection and conflict resolution stages, a greedy strategy-based Q-value sorting mechanism is introduced. It selects the action with the highest Q-value as the processing decision of the device for the current slot, and resolves wireless channel and edge server resource selection conflicts caused by multi-device processing decision coupling through edge-end collaboration. In the learning stage, considering the importance of different device services and the confidence of action samples, a dual replay experience pool is designed to ensure sample diversity, effectively avoiding data loss potentially caused by aggressive strategies. This greatly improves the convergence of the algorithm. The proposed algorithm ensures the orderly operation of cloud-edge-end services in distribution networks.

Finally, the effectiveness and rationality of the proposed algorithm are verified through simulation examples. The simulation results show that the proposed algorithm can increase the amount of cloud-edge-end collaborative processed data by 11.71% and 14.86%, reduce queuing delay by 24.68% and 26.09%. It can also increase the average data acquisition volume by 8.87% and 7.44%. At the same time, it significantly reduces the backlog of device layer queue backlog and greatly improves the convergence speed of the algorithm. The author team will further consider information synchronization and security issues during data transmission and processing.

Hydrogen, as a clean, efficient, and high-quality energy source, is recognized as a crucial solution for decarbonizing the energy system and mitigating climate change. The electricity and hydrogen energy system, which uses electricity and hydrogen as energy carriers, represents a key pathway for integrating power systems with hydrogen energy. It helps overcome the developmental limitations of renewable energy, fosters the interconnection and complementarity of multiple energy modes, and promotes deep integration across generation, grid, load, and storage. The electro-hydrogen coupling process, central to this system, can lower operating costs through peak shaving and valley filling. However, the efficiency of electrolyzers and fuel cell remain suboptimal, resulting in significant exergy losses alongside economic benefits during the coupling process. Striking a balance between economic viability and energy saving continues to be a challenging task. Moreover, the substantial forecasting errors caused by the uncertainty of renewable energy outputs can negatively impact the supply-demand balance and the operating conditions of electrolyzers. Therefore, the uncertainty risks associated with renewable energy must be thoroughly considered in optimal scheduling. In response to the above problems, a robust optimal scheduling model based on exergoeconomic analysis is proposed, with the uncertainty set defined by the confidence interval to reduce the conservatism of robust optimization.

Firstly, considering the dynamic efficiency characteristics of the electrolyzer, piecewise linearization was applied to handle the non-convex terms introduced by this relationship. The operation model of the electrolyzer including hydrogen production power allocation and operation models of fuel cell and energy storage equipment were constructed. Secondly, the energy quality coefficients were employed to analyze the exergy loss distribution based on the equipment operation model. A cost accounting method for exergy losses, including both internal and external factors, was proposed. Internally, the cost allocation method was used to price unit exergy losses, enabling the calculation of operational loss costs based on the distribution of exergy losses. Externally, the cost of transmission line losses and penalties of wind curtailment were calculated according to current electricity prices and relevant policies. Thirdly, taking into account constraints such as electrolyzer start-stop cycles, ramping power, and energy balance, an optimal scheduling model was developed with the goal of minimizing total exergy loss costs in the electricity and hydrogen energy system. Then, the model was reformulated into a robust optimization problem based on the uncertainty set of the confidence interval，and a dual transformation method for solving the model was proposed.

In the case simulation, four cases are set up for comparative analysis, leading to the following conclusions: (1) By setting the wind curtailment penalty coefficient appropriately, with the goal of minimizing exergy loss costs, a balance can be achieved between the economic benefits and the exergy losses associated with the electricity-hydrogen coupling process, while ensuring the efficient absorption of wind power. (2) The proposed model can further improve the overall hydrogen production efficiency of the electrolyzer array by taking advantage of the flexibility of hydrogen production power allocation. (3) The confidence interval is used as the uncertainty set of robust optimization, which can take into account the probability characteristics of random variables, and reduce the conservative degree of system operation under the premise of ensuring robustness.

Under the background of the dual carbon goals, the regional integrated energy system (RIES) can achieve interconversion between heterogeneous energy sources due to its multi-energy coupling characteristics, providing new technical support for energy-saving and efficient operation of modern energy systems. Due to the differences in the flow of heterogeneous energy sources in transmission pipelines, existing research usually adopts convex relaxation techniques or linearization methods to model and solve the RIES for multi-time-scale, and relies on high-precision source-load forecasting results and equipment mathematical modeling to improve the reliability of scheduling decisions. However, the increasingly complex internal energy coupling structure of the RIES has increased the difficulty of its refined mathematical modeling and solution, posing challenges to the real-time scheduling decisions and safe optimal operation of the RIES. therefore, this paper proposes an improved distributed bi-layer proximal policy optimization (DBLPPO) deep reinforcement learning scheduling model. This model can achieve multi-time-scale optimization management of various energy networks in the RIES and avoid the optimization difficulties caused by non-convex nonlinear model structures in scheduling solutions.

Firstly, the power output, storage, and transformation of internal energy in the RIES are constructed into a high-dimensional space Markov decision process mathematical model. Secondly, based on the improved distributed proximal policy optimization (DPPO) algorithm, a sequential decision description is made for it, and a control model of the internal bi-layer proximal policy optimization (PPO) is constructed. the local network adopts the "coupling first, then decoupling" solution approach to carry out multi-time-scale optimization decision-making for the cold-heat system and the power system. In the early stage of long time scale, the inner and outer models perform coupled solutions, and the RIES cold-heat system and power system achieve coordinated optimal operation. In the remaining short time scales, the inner and outer models perform decoupled solutions and carry out short-term flexible regulation of the power system. the inner and outer models interact with each other and fluctuating convergence towards the reward maximization direction, eventually achieving multi-time-scale optimization scheduling of the RIES cold-heat system and power system.

This paper conducts simulation experiments with a cold-heat-electric RIES as the scheduling scenario, and compares the scheduling results of the DBLPPO scheduling model with those of a single time scale scheduling model (PPO, DPPO). the results show that the DBLPPO scheduling model can flexibly regulate the system's adjustable resources in the short time scale, meet the power fluctuation requirements of electricity, heat, and cold loads in the short time scale, and has the lowest comprehensive operating cost, which is 24.47% lower than that of the DPPO scheduling model and 28.54% lower than that of the PPO scheduling model. In addition, simulation experiments are conducted with the DBLPPO scheduling model and the bi-layer PPO scheduling model in the same scenario, and the results show that the distributed structure of the DBLPPO scheduling model still has a significant advantage in improving model training efficiency, which can effectively shorten the training time, 10.01% shorter than that of the dual-layer PPO scheduling model.

Through case analysis, it is verified that the proposed scheduling model can achieve coordinated optimal management of various energy networks in the RIES at different time scales, accelerate the optimal decision-making speed of the multi-time-scale scheduling model, and by virtue of the fast adaptability of the deep reinforcement learning algorithm, efficiently solve random optimization problems in complex RIES scenarios, and improve the economic benefits of system operation. The next step of work will be to improve the model to enhance the environmental awareness ability of the inner model, so that its decision-making scheme is always the optimal scheduling decision in the long time scale.

With the development of the national unified electricity market, the market scale has gradually increased. Take inter-provincial medium- and long-term power transaction as an example, the number of market participants is over a thousand. The similarity of the bid prices of numerous market participants is likely to lead to multiple purchasing and selling pairs having the same social welfare. As a result, the market clearing problem that maximizes social welfare may have multiple optimal solutions. To ensure the effectiveness and fairness of market clearing, the multi-level objective sequential optimization should be implemented under multiple-solution scenarios. However, the existing methods based on multi-objective optimization cannot balance effectiveness and efficiency. To address this issue, take inter-provincial medium- and long-term power transaction that may have multiple solutions as a research objective, an efficient multi-level objective sequential optimization method is proposed in this paper. The main contributions are illustrated as follows:

First, the market clearing model with multi-level objective sequential optimization is established. Four objective functions are considered according to the industrial practices, including maximizing social welfare, maximizing transaction volume of renewable energy, maximizing total transaction volume, and equally distributing tradable power among purchasing and selling pairs with the same social welfare. Market clearing models considering the aforementioned four objective functions are separately established. The optimal objective functions of the preorder model are used as the operating constraints of the subsequent model to ensure the optimality of the objective functions with high priorities. By sequential solving these four market clearing models, the market clearing effectiveness and fairness can be guaranteed even under multiple-solution scenarios.

Second, the multiple-solution judgment auxiliary optimization model for the market clearing problem is established based on the bound constraints of the optimal solution, according to which the multiple-solution characteristics of market clearing problems can be recognized. The recognized multiple-solution characteristics can provide support for market operators to design the measure for handling multiple-solution scenarios. For instance, more objective functions can be introduced if multiple-solution scenarios cannot be effectively avoided after the sequential optimization of four objective functions. Besides, regarding the computational burden caused by the solution to four market clearing models, the multiple-solution judgment auxiliary optimization model is embedded into the sequential optimization process to simplify the clearing process by avoiding unnecessary optimization.

Third, to meet the calculation efficiency demand, the lossless acceleration method for market clearing based on solution information of the preorder model is proposed. For the market clearing models with multi-level objective functions, the optimal solution of the preorder model is used as the high-quality initial feasible solution of the subsequent model, which can guide the warm-start accelerating process of the subsequent model without the loss of accuracy. For the multiple-solution judgment auxiliary optimization model, the optimal solution of the preorder model is used as the initial feasible solution. Based on this, the termination criterion for the calculation process is established according to the comparison between the initial objective function and the current objective function. In this way, the judgment process can be accelerated without affecting judgment accuracy.

Finally, case studies based on practical inter-provincial medium- and long-term transaction data in China demonstrate that the proposed method can greatly improve the market clearing effect for the subordinate objectives while ensuring the optimality of the primary objective. In addition, benefiting from the proposed model solution acceleration strategy and the sequential optimization process simplification strategy, the market clearing efficiency can be improved by 37 times without the loss of accuracy under the typical scenario.

As electric vehicles (EVs) achieve higher penetration, their potential as mobile energy storage systems for auxiliary frequency control becomes increasingly evident. However, uncertainties in EV user behaviors, such as irregular charging patterns and diverse preferences, present challenges to fully utilizing their frequency regulation capabilities. This study proposes a power boundary description model and frequency support strategy for EVs, integrating user-specific characteristics and preferences to address these issues.

The research begins with a detailed analysis of uncertainties related to EV user behaviors, battery capacities, and charging/discharging rates. A Gaussian mixture distribution method is employed to model these uncertainties, capturing the probabilistic variability inherent in user behavior. To further refine the model, a Logit framework predicts the schedulability of EVs, accounting for user willingness to participate in grid services based on factors such as charging convenience and state-of-charge (SOC) preferences.

Building on this foundation, the study develops a dynamic EV regulation boundary model that reflects user preferences and behavior characteristics. By adjusting the upper and lower limits of power fluctuations, the model defines flexible boundaries tailored to individual user needs. This approach ensures an upward trend in users’ SOC during participation in grid services, preventing excessive battery depletion and enhancing user satisfaction. The regulation strategy dynamically adjusts to user-defined constraints, enabling effective participation in grid frequency control while respecting user autonomy.

To validate the feasibility of the proposed method, simulations are conducted under various scenarios. The results demonstrate that the regulation strategy significantly improves frequency stability metrics. Compared to conventional methods, the proposed approach reduces maximum and minimum frequency deviations by 13.91% and 29.27%, respectively, and decreases the root mean square frequency deviation by up to 29.59%. The method also shortens the duration of extreme frequency deviations by 42.69%, showcasing its ability to enhance grid frequency stability while minimizing disruptions to user operations.

This study also examines the broader implications of integrating user-specific characteristics into EV frequency regulation. By ensuring a balance between grid stability and user satisfaction, the proposed strategy highlights the potential of EV fleets as flexible and reliable grid resources. The findings emphasize the role of EVs in supporting renewable energy integration, mitigating the challenges posed by the variability of wind and solar power. In conclusion, the study provides a comprehensive framework for characterizing EV power boundaries and developing frequency support strategies. By incorporating user behavior and preferences into the control process, the proposed method offers a practical solution to the challenges of large-scale EV integration. These results contribute to the advancement of smart grid technologies and provide valuable insights for policymakers and grid operators aiming to maximize the benefits of EV participation in modern power systems.

Electric car-sharing (ECS), as a component of the sharing economy, is of great significance in alleviating urban traffic congestion and reducing carbon emissions. Electric car-sharing system (ECSS) involves multiple entities such as users, operators and power grids. At present, one-way network operation mode is mostly adopted. Users can pick up and return vehicles at any network specified by the operator, and the operator arranges for vehicles in the network to connect to the power grid for charging. The optimal scheduling of urban electric car-sharing system is needed to solve the increasingly prominent problems such as imbalance between user demand and station cars supply, and mismatch between cars charging and grid operation status. Current strategies for vehicle scheduling are high-cost and coercive, while charging scheduling only ensures vehicle availability, lacking consideration of the impact of vehicle charging on the grid. Addressing these issues, the application of low-cost, non-coercive nudging methods from behavioral economics in the field of ECSS was explored and a coordinated user nudging and charging optimization scheduling method for urban shared electric vehicles was proposed.

Firstly, at the level of vehicle scheduling with supply and demand balance, nudging was used to guide user dispatch. Based on actual surveys, the main factors influencing users' choice of return points were identified, and nudging schemes for strong and weak scenarios were designed based on a framework of motivational and cognitive nudges. The revealed fuzzy comprehensive evaluation method (r-FCEM) was used to evaluate the user responsiveness to the nudging schemes, determining the probability of users participating in vehicle dispatch, thereby relocating vehicles from surplus supply points to stations with high demand, and improving operators' rental service income. And then we tested the feasibility of the nudging scheme and found that the design of the nudging scheme for users' choice of return stations can effectively improve user responsiveness and has a certain degree of feasibility.

Secondly, for the charging scheduling problem, nudge guided users to return vehicles to low-cost, low-carbon stations, and charging optimization model considering economic and low-carbon factors was designed. Based on deep Q network (DQN), an ECSS operating environment was constructed to simulate the interactions among users, operators, and the grid. After training process, coordinated solutions for nudging and charging optimization were obtained. This resulted in a dispatch plan for vehicle scheduling and a charging schedule for charging optimization.

The research first examined the number of vehicles and the travel and arrival volumes at typical stations under nudged and non-nudged scenarios, demonstrating the impact of nudging on supply-demand imbalance and charging optimization issues. It was found that user nudging can alleviate phenomena of under-supply and surplus, guiding vehicles to low-cost, low-carbon stations. Then, four scenarios were set up, revealing that single vehicle scheduling and charging scheduling alone offer limited improvement to the economic benefits of ECSS. It is necessary to solve nudging and charging scheduling in a coordinated manner to enhance user responsiveness through non-coercive strategies, reduce grid load fluctuations, and comprehensively improve the economic efficiency of operators while addressing vehicle scheduling and charging optimization problems.

Future work on nudging will expand the scope and number of questionnaire surveys to further validate the feasibility and effectiveness of practical applications. Algorithmically, future research will focus on refined modeling for large-scale ECSS operations and seek better algorithms to adapt to large-scale scenarios.

During the long operational time, porcelain insulators are subjected to a synergistic effect of the electrical, thermal, mechanical stresses, and environmental factors, which causes insulation degradation and lead to low and zero resistance insulators. Compared to traditional methods, infrared imaging has been widely used in the detection of deteriorated insulators and surface contamination because of its advantages of non-stop operation, non-contact and anti-electromagnetic interference. However, there is limited research on the impact of contamination on the heating characteristics of degraded insulators. Moreover, there is a lack of research on the effects of different types of contamination (category A and category B) on the heating characteristics of the insulators and the infrared detection of degraded insulators. In response to the above issues, the effects of contamination level, deterioration resistance and the location of deteriorated unit on infrared detection of the insulators were investigated through field tests and simulation analysis, obtaining the heating patterns of deteriorated insulators under different pollution conditions.

Firstly, the relationship between temperature rise, deterioration and surface contamination was explored through a heating model of porcelain insulator. Secondly, a test platform was built to simulate the operating conditions of 110 kV insulators, and the infrared imaging patterns of insulator strings were analyzed by changing the level of contamination resistance of deteriorated insulator, and the position of degraded insulators in the string. Finally, a thermal-electric coupling model of the insulator was established using finite element method to analyze and calculate the temperature distribution of insulator strings under the combined effects of dielectric loss, conduction current and heat conduction. This model is then used to validate the experimental results.

The results show that the temperature rise of the insulator in the string initially increases and then decreases with the decrease in its resistance. The maximum temperature rise and temperature growth rate of degraded piece with the same resistance value located at the high-voltage end are higher than those of degraded piece located at the medium-voltage end and the ground end, with temperature change rates of 0.093, 0.04 and 0.06, respectively. The overall temperature of steel cap increases with the increase in category A contamination. When the surface wet contamination is relatively light, the temperature variation rate range of each piece in the string is 0.014~0.107, while under severe wet contamination, it ranges from 0.087 to 0.12. The conductivity of fog water (category B contamination) has a significant impact on the temperature rise of the insulator, which increases with the increment of fog water conductivity. Taking the temperature rise under no salt fog condition as the benchmark, the overall average temperature change rates under fog water conductivities of 0.6, 2.2 and 4.1 S/m are 54.9%, 101.2% and 153.9%, respectively.

The following conclusions can be drawn from the test results and simulation analysis: (1) The impact of dry contamination on the heating of deteriorated insulator is negligible. Under conditions of fixed category B contamination, the effect of category A wet contamination on heating is related to the position of deteriorated piece in the string. Furthermore, as the degree of wet contamination increases, the temperature of each piece tends to be consistent. (2) Fog water conductivity (category B contamination) has an additional effect on salt deposit density, which further affects the temperature rise of the insulator by increasing the number of conductive ions. There is a saturation phenomenon in insulator temperature rise in salt fog environments. (3) The excessive humidity can cause disordered temperature changes on the insulator surface, therefore, humidity greater than 90% is not considered during the detection. The leeward side, with small amount of contamination, is selected as the infrared observation position to more clearly diagnose deteriorated insulator in the string.

PPTA is a high-insulation, high-modulus fiber material that is widely utilized in the insulation protection of power equipment. However, its inherently low thermal conductivity limits the ability to dissipate heat effectively. Recently, nano-doping modification and coupling agent grafting have emerged as effective methods for enhancing the thermal properties of high polymers. BN is an inorganic filler with favorable thermodynamic properties while there is limited research on BN modified with coupling agents doped PPTA. To investigate the impact of BN fillers modified with different silane coupling agents on the thermomechanical properties of aramid composites, four types of silane coupling agents (KH550, KH560, KH580, and QX1324) were selected, various modified BN composite models were created by doping para-aramid (PPTA) using Materials Studio. The thermal conductivity, glass transition temperature, mechanical properties, and intermolecular interactions of the composite models were analyzed by the molecular dynamics method.

Firstly, the thermal conductivity of the composite system was calculated using the rNEMD method. The thermal conductivity of the composite systems modified with coupling agents were significantly enhanced. Specifically, the thermal conductivity of BN-KH560/PPTA and BN-QX1324/PPTA increased by 83.05% and 74.58%, respectively, compared with pure PPTA. RDF analysis indicated that the interaction between the end group of KH560 and QX1324 coupling agents and PPTA was more pronounced. Additionally, the glass transition temperature of the composite system was analyzed by the specific volume-temperature method, the BN-QX1324/PPTA system reached 597.746 K, which represented a 12.93% increase.

Regarding mechanical properties, the Young's modulus and shear modulus of the composite systems were consistently higher than those of pure PPTA over the temperature range from 300 K to 700 K. At 300 K, the Young's modulus of the BN/PPTA, BN-KH550/PPTA, BN-KH560/PPTA, BN-KH580/PPTA, and BN-QX1324/PPTA systems was, on average, 9.95% higher compared to PPTA. Furthermore, the BN-QX1324/PPTA system demonstrated greater resistance to the degradation of mechanical properties at high temperature.

Regarding structural parameters, the reasons for the improved performance of the composite systems were elucidated through calculations of cohesive energy density, free volume fraction, hydrogen bond number, and other parameters that assessed intermolecular interactions. Modified BN enhanced the cohesive energy density of the systems through hydrogen bonding and van der Waals force, further strengthening the interaction within the composite systems. Notably, due to the strong electronegativity of fluorine groups, the cohesive energy density of the BN-QX1324/PPTA system increased the most, with an average rise of 13.32%. Additionally, it exhibited strong resistance to external electric field interference.

To verify the validity of the calculation results, the BN-QX1324/PPTA system, which showed the best modification effects in the simulation, was selected for experimental investigation. The results indicated that the thermal conductivity, glass transition temperature, Young's modulus, and breakdown field strength of the PPTA/BN-F system were significantly improved that compared to the pre-modification values, and the trends were consistent with the simulation results. This study validates the reliability of the simulation calculations and show that the enhanced intermolecular interactions between the fluorinated group and PPTA in the QX1324 coupling agent are the underlying reasons for the observed performance improvements.

Meta-aramid (PMIA) is a unique fiber that possesses exceptional insulation strength and thermodynamic stability. It is widely regarded as an ideal material for the development of the next generation of insulation paper. However, its intrinsic thermal conductivity of 0.21 W/(m·K) is relatively low and may not meet the long-term service requirements in high-temperature environments. To enhance the thermal conductivity and insulation of the PMIA paper, AlN and BN fillers are selected for composite doping modification of PMIA paper. The surfaces of the two fillers are coated with polydopamine (PDA) and modified with a KH550 silane coupling agent to improve the dispersibility of the two fillers. By adjusting the doping ratio, AlN-BN/PMIA composite insulation paper with different concentrations was prepared. The microstructure was characterized and the breakdown strength, conductivity, and thermal conductivity were tested. The effect of two different filler ratios on the insulation and thermal conductivity of the material was studied.

Firstly, the surfaces of the two fillers are coated with polydopamine (PDA) and modified with a KH550 silane coupling agent to enhance their dispersibility. By adjusting the doping ratio, AlN-BN/PMIA composite insulation paper with different concentrations is prepared. Secondly, the microstructure of samples is characterized and the breakdown voltage, conductivity, and thermal conductivity are tested. The influence of the ratio of two fillers on the insulation and thermal conductivity of the material was studied. Thirdly, based on density functional theory, band structure calculation and analysis are conducted, and a design concept of a “stepped charge trap” is proposed. In addition, the composite breakdown model is constructed using the phase field method, explaining the inherent mechanism of performance improvement.

According to the test results, adding BN to the AlN filler can further improve the matrix structure and fix the damage caused by the high concentration aggregation of AlN. The surface of the composite material appears relatively dense when the AlN/BN ratio is 3:7, with only a small amount of PMIA fibers and fillers precipitated. At a mass fraction of 40%, the breakdown strength of the composite gradually increases as the BN doping ratio increases. At a ratio of AlN/BN of 3:7, the composite paper exhibits its maximum breakdown strength of 186 kV/mm, which is 66.07% higher than that of the pure PMIA sample. Additionally, the conductivity of the composite is at its lowest value during this ratio. On the other hand, at an AlN/BN ratio of 7:3, the thermal conductivity of the composite is optimal, increasing by 213.6% compared to pure PMIA samples. The high aspect ratio structure of BN links it with AlN fillers to form an “thermal conductivity network”, which increases the thermal conductivity.

Energy band structure analysis based on density functional theory suggests that the wide bandgap properties of AlN and BN result in the formation of “stepped traps” at the PMIA interface. This leads to an increased energy barrier for charge transitions and limits the migration of charge carriers. In addition, a phase field simulation model indicates that the introduction of BN can further homogenize the electric field distribution, reduce the degree of local polarization, and thus enhance the insulation performance of the composite system.

Frequency domain dielectric spectroscopy (FDS) is widely used for condition diagnosis of oil-paper insulated power equipment due to its high measurement accuracy and ease of operation. However, in winter in Northeast and Northwest China, the temperatures remain below -40℃ for extended periods, and rapid internal cooling of equipment during maintenance can lead to water crystallization and partial solidification of transformer oil, severely affecting accuracy of FDS results. In order to improve the accuracy of oil-paper insulation condition assessment, it is necessary to perform temperature normalization of the test results. However, traditional "master curve" methods are unsuitable for low-temperature environments as they produce significant errors in high and low frequency ranges. Existing research rarely focuses on the dielectric response characteristics and assessment methods of oil-paper insulation in extremely low-temperature environments. Therefore, this paper studies the FDS results of oil paper insulation at different temperature, and establishes a new temperature normalization model by Havriliak-Negami (H-N) model. This model improves the temperature normalization accuracy, filling the gap in the assessment of oil-paper insulation condition in low-temperature environments.

Firstly, starting from the physics of dielectrics, derive the effects of temperature on the relaxation processes. to obtain the formula for temperature normalization parameters. Samples of oil paper insulation with different moisture contents (0.41%~3.91%) are prepared, and an experimental platform for high and low temperature dielectric response testing is set up. By measuring the frequency dielectric spectra (1 mHz~5 kHz) of samples with different moisture contents at various temperatures (-40~30℃), it is found that as the moisture content gradually increases, the dielectric loss values also increase. Additionally, the decrease in temperature tends to make the high-frequency FDS results more consistent the relaxation peaks less distinct, which increases the difficulty of assessing moisture content. In order to understand the changes in the internal water morphology of oil-paper insulation at low temperatures, the distribution of moisture within insulation paper is studied using isothermal adsorption experiments, revealing a substantial amount of free water attached to cellulose fibers. At low temperature, this part of water crystallizes and precipitates, which affects internal polarization processes of oil paper insulation. Using thermally stimulated depolarization current (TSDC), it is discovered that concentration polarization, interfacial polarization, and dipole polarization are the main three polarization processes in oil-paper insulation. Based on the extended derivative method, it is found that as the temperature decreases, the intensity of concentration polarization gradually weakens, and the relaxation time of interfacial polarization decreases.

In order to study different polarization processes separately, the improved Havriliak-Negami (H-N) model is used to decompose FDS results, extracting characteristic parameters of each relaxation process. It is discovered that as temperature decreases, concentration polarization diminishes and disappears below 0℃, temperature only changes the relaxation time of interfacial polarization without altering its strength, and dipole polarization intensifies due to reduced molecular thermal motion. Moreover, the conductivity process, influenced by both ionic and electrophoretic conductivity, gradually decreases and stabilizes. At the same time, temperature normalization parameters for each relaxation process are extracted.

Finally, a new temperature normalization method is proposed based on the characteristics of each polarization process. Compared to the traditional “master curve” method, this method has higher accuracy in low temperature environments and in conditions with high moisture content. In low temperature, this method maintains high accuracy with a goodness of fit of 0.975 7, compared to 0.952 6 with the traditional method, In samples with high moisture content, the goodness of fit is 0.982 2. At the same time, 5 to 7 more frequency data points are added and full-frequency range correction is achieved, solving the issues of large low-temperature correction errors and insufficient frequency data in the “master curve” method.

Polymer materials are widely used in power electronics and power transmission and distribution systems because of their excellent dielectric properties. However, under the long-term coupling effect of mechanical stress, thermal effect, electrical stress and other factors inside the insulation material, it is easy to cause the growth of electrical trees, which will cause internal damage and deterioration of the material, and eventually lead to the harm caused by penetrating discharge. The numerical simulation method can provide reference for improving the insulation reliability of the system. However, some key parameters of the existing model are difficult to obtain directly from the experiment, and can only be realized through the model verification to achieve the microscopic electrical tree simulation of specific materials, and can not achieve the engineering tasks from material parameter testing to complex structure electrical tree prediction. Therefore, this paper aims to propose a method of electrical tree limb simulation with simple model and parameters that can be obtained by experiment, so as to improve the engineering applicability of electrical tree limb simulation.

The inverse power law is a phenomenological model directly based on the lifetime data of solid dielectric, which describes the physical process of the accumulation of electrical damage in solid dielectric to the generation of penetrating tree channels, and has the theoretical basis for describing the growth of electric treees. Therefore, this paper analyzes the physical relationship between the parameters of the inverse power model and the growth law of electrical trees, establishes the basic equation of local electrical damage based on the inverse power model, and establishes the electric tree simulation method based on the inverse power model combined with the electric field calculation and the material dispersion equation. Further, a sample of pin-plate electrode is used to demonstrate how to simulate the electrical tree by testing the basic parameters of the material. The experimental verification of the simulated results of electric treees is carried out, and the simulated growth law of electric treees is compared with the experimental growth law of electric treees. Finally, the difference between the proposed method and the phase-field simulation and WZ model is compared.

The final results show that the method proposed in this paper can effectively simulate the electrical trees by using the experimental material parameters. The simulated electrical trees in this paper agree with the experimental results in terms of morphology and growth law. In this method, the shape of electrical trees is correlated with voltage tolerance index and cumulative damage standard deviation, and the growth rate of electrical trees is correlated with voltage tolerance index and cumulative damage mean. Compared with the phase-field simulation and WZ model, the proposed method can simulate the gradual growth of electrical trees, and the model parameters can be obtained experimentally.

Gas insulated switchgear (GIS) faults occur frequently after live operation, and according to statistics, the faults after live operation account for 60% of the total. The strong shock vibration generated by GIS live operation not only produces metal contaminants, but also activates latent particles, which seriously affects the insulation safety of GIS. The physical effects generated by GIS live operation are complicated, mainly including shock vibration, overvoltage, residual voltage and other physical effects. The mechanism by which the particles inside the GIS are affected by switching operation is unknown and is extremely dangerous. The mechanism of switching operation on particles in GIS is unknown and extremely dangerous.

In order to solve the problem that particles are prone to discharge after live operation, and to clarify the motion mechanism of spherical metal particles in GIS after impact vibration, this paper carries out the following work: firstly, we study the propagation process of vibration inside the cavity, and then we carry out the forced correction on the basis of the flexible boundary three-dimensional cylindrical vibration model, which improves the equivalence with the actual working conditions. The propagation form and the "ripple" propagation behavior of shock vibration in GIS are revealed, and it is clarified that the vibration wave is divided into P-wave and S-wave during the propagation process, the P-wave propagates faster than the S-wave, and the S-wave plays a major role in the jumping of the particles. And then the energy transfer process of the cavity and particles after being vibrated is analyzed, and the excitation effect of the shock vibration on the system is analyzed in the form of energy as a link. The energy transformation of the system after the vibration work is clarified, the cavity-particle kinetic energy transfer conservation model is constructed, and the activation conditions of the particles are defined with the help of the jumping field strength, and the activation criterion of the particles subjected to vibration is proposed. On the basis of the aforementioned research, the whole process model of particle motion under typical vibration excitation of live operation is established by considering the particle charge motion characteristics and collision random characteristics, and verified by experiments. On the basis of the validation, the correction considering the microscopic force is carried out, and the error is analyzed from the microscopic point of view.

The model fully considers the influence of different particle sizes and materials on the particle dynamics, and is more equivalent to the actual working conditions than the method defined by the recovery coefficient. Finally, it is clarified that the vibration excitation can activate the motion of the particles, and the influencing factors of the motion behavior of the particles are explored. Combined with the propagation and attenuation characteristics of vibration, the criterion of critical activation distance of particles under charged working conditions is proposed, and the changes of critical activation distance of particles under different factors are calculated. The study shows that the critical activation distance increases logarithmically with the increase of voltage amplitude and vibration intensity. This study provides support for solving the problem that particles are very likely to cause discharge faults after GIS live operation.

The low temperature and high humidity environment in winter can easily cause wind turbine blades to freeze, seriously affecting the actual power output and safe operation of wind turbines. To avoid problems such as increased fatigue load and vibration of unit components caused by icing, wind farms need to implement shutdown strategies in a timely manner based on the icing situation of the blades. Therefore, accurate identification of blade icing status has become one of the key points in maintaining the safe operation of winter wind turbines. However, current ice diagnosis methods rely on a large amount of time series data for modeling and prediction. In practical work, due to equipment and working conditions, it is difficult to collect sufficient ice sample monitoring data, which leads to the widespread problem of data imbalance and has a continuous impact on the improvement of ice diagnosis accuracy. To solve this problem, this paper proposes a fusion diagnostic model based on conditional generative adversarial network (CTGAN) and light gradient boosting machine (LightGBM), aiming to achieve high-performance wind turbine blade ice diagnosis using a small number of training samples.

Firstly, based on the sliding window algorithm, new mixed features are further constructed on the basis of the original features. Secondly, the CTGAN model is used to learn the data distribution of real samples, and Nash equilibrium is achieved through adversarial training with generators and discriminators, generating new samples that are similar to real samples. Then, the synthesized samples are input into LightGBM to extract effective features and diagnose icing, and the LightGBM model is modified by introducing a focus loss function to improve its ability to distinguish confusing samples. Finally, the attribution theory based on shapley additive explanetions (SHAP) was used to analyze the factors affecting icing.

The simulation results on actual wind farm data show that the diagnostic accuracy of all algorithms has a certain improvement effect after using mixed features, and the average diagnostic accuracy of each model can reach 0.979. Due to the introduction of sample expansion algorithms, the accuracy of each model has improved to varying degrees compared to when data is lacking. When the sample imbalance rate is 30%, the accuracy of the traditional Logistic regression classification model is improved by 11.02%. At the same time, the accuracy of LightGBM (Focal Loss) is 0.982, which is close to the accuracy when the sample is sufficient. As the sample imbalance rate decreases and the actual number of ice-covered samples further decreases, the advantages of the sample expansion algorithm gradually become apparent. When the sample imbalance rate is 10%, compared to the unexpanded samples, the accuracy of Logistic regression model is improved by 13.55%. When the sample imbalance rate is 5% and the actual number of ice-covered samples is only 15, compared to the unexpanded samples, the accuracy of Logistic regression, KNN, XGBoost, and LightGBM models has improved by 35.85%, 4.52%, 9.32%, and 9.18%, respectively. This indicates that CTGAN has good sample generation ability and can effectively learn the distribution of real samples even when the sample data is small.

From the simulation analysis, the following conclusions can be drawn: (1) The mixed features constructed based on the sliding window algorithm in this paper can significantly improve the classification ability of each model. At the same time, the LightGBM model combined with mixed feature information has obvious advantages compared to other models. (2) The sample generation model CTGAN can effectively learn the distribution of real samples, and compared to other data augmentation methods, it can generate new samples that are more similar to real samples. (3) By using the Focal loss function to modify the LightGBM model, the model's ability to distinguish easily confused samples has been increased. In addition, based on the SHAP attribution theory, the importance of each icing factor was analyzed, and the quantitative impact of key features on the diagnostic results was quantified, improving the credibility of the model's diagnostic results.

ZnO-based functional ceramics are widely used in the fields of varistor, thermistor, and gas-sensing. However, the temperature required for the preparation of ZnO-based functional ceramic is still high (>1 000℃). As a result, the additives that lead to modulating the properties of ceramic materials are limited to inorganic fillers. Conventional sintering leads to excessive growth of ZnO grains, which makes it difficult to achieve the miniaturization requirements of ZnO-based functional ceramic devices. Cold sintering process (CSP) enables the densification of ceramic materials at temperatures of below 300°C, thus providing the possibility for grain boundary engineering using ceramic materials as the matrix with organic polymer fillers or organic/inorganic composite fillers.

In this paper, zinc oxide (ZnO)-polytetrafluoroethylene (PTFE)-based ceramic composites were prepared by CSP. Based on the above cold sintering conditions, high-density (>97%) ZnO-PTFE composite ceramics were prepared with ZnO as the matrix and polytetrafluoroethylene (PTFE) as the filler. The electrical properties of the ZnO-PTFE specimens showed better non-ohmic characteristics at the polymer content of 15%, the breakdown field and nonlinear coefficient of the composites can reach 933.68 V/mm and 5.74. The breakdown field is 6.92 times higher than the classical five-element formulation of ZnO varistor (135 V/mm), but the nonlinear coefficient is low. Microstructure observation and impedance performance testing showed that the PTFE phase limits the grain growth and increases the ceramic grain boundary impedance. PTFE at grain boundaries can induce the formation of varistor properties of ZnO-based composite ceramics, and improve the flexibility of ZnO ceramics.

Further, the effects of metal oxides and PTFE on the microstructures and electrical properties of ZnO-PTFE based composites were investigated. The results indicate that a high relative density of over 97% was achieved for ZnO-PTFE-based composites doped with PTFE or co-doped with PTFE and metal oxides (CoO, Mn₂O₃). It is found that the electrical properties of ZnO-PTFE-based composites were significantly enhanced with the co-doping of PTFE, CoO, and Mn₂O₃. Specifically, the breakdown field and nonlinear coefficient of the composites were improved to 3 555.56 V/mm and 13.55, respectively. The J-E results show that the electrical conduction of the ceramic composites were dominated by the thermionic field emission at grain boundary. Moreover, the elastic modulus of the ceramic composites decreases greatly with the addition of PTFE and then increases after doping metal oxides (CoO, Mn₂O₃).

This study demonstrates that CSP provides a new route to fabricate ceramic-polymer-based composites and modulate their properties.