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.

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.

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.

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.

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.

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.

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.

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.

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.

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.