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

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

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

With the 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.

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.

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.

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.

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

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

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

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

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.

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

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

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

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

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

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

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

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.