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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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