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.

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.

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.

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.

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.

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.

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.

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.

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.