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.

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.

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.

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.

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.

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.

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.

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.

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.

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.