The output characteristics of magnetostrictive devices usually show a strong bias condition dependence. The bias magnetic field provided by constant current will change nonlinearly with the change of the material permeability under stress excitation (manifested as non-constant bias magnetic field). This change affects the accurate characterization of material magnetization process and the rational design of bias points. At present, the inverse effect models of magnetostriction mainly focus on material characterization under constant bias magnetic field. Models that solely consider a constant magnetic field fail to accurately reflect the output characteristics of devices in their actual operating environments. Establishing a hysteresis model that accounts for the inverse magnetostrictive effect with dynamic variations in the bias magnetic field holds significant research importance.

The models established in this paper include the average model of non-hysteresis energy, the hysteresis constitutive model and the equivalent magnetic circuit model taking into account the variation of the bias magnetic field. Firstly, based on the free energy theory, the expression of the non-hysteretic magnetization is derived, and the average model of the non-hysteretic energy is established. Secondly, using the modeling idea of J-A model for positive hysteresis phenomenon, the first order differential equations of irreversible component M_irr, λ_irr and stress are introduced. The hysteresis constitutive model which can characterize the inverse effect of magnetostrictive materials is obtained. Based on the equivalent theory of magnetic circuit, the influence of stress on magnetic field strength is reflected by the change of magnetoresistance. Finally, an energy average hysteresis model is established which can account for the change of bias magnetic field. Hysteresis models often have difficulty in parameter identification. An improved cuckoo search-grey wolf optimizer (CS-GWO) hybrid algorithm is proposed by introducing nonlinear adaptive step factor α(t). Comparing the optimization results of the traditional optimization algorithms of CS, GWO, and PSO, the CS-GWO algorithm has the highest accuracy and the fastest convergence speed, and can accurately and efficiently identify the globally optimal parameters of the energy-averaged hysteresis model. Model validation is performed in two steps. First, the basic parameters of the model were extracted based on the experiments of Fe₈₁Ga₁₉ alloy bar under -115~0 MPa compressive stress and 22.3~446 Oe constant bias magnetic field. The error between the B-σ curve simulated by the model and the existing experimental data is only 3.85%, which is better than the error calculated by the traditional model of 6.79%. The error of ε-σ curve simulated by the model is 2.93%. Then, based on the experimental data of Fe₈₁Ga₁₉ alloy bar under constant current bias, the parameter H_s is further extracted. The errors of the simulated H-σ curve, B-σ curve and ε-σ curve with experimental data are 4.74%, 4.31% and 3.97%, respectively. The simulation results can accurately describe the tendency of the bias field to increase nonlinearly with the increase of stress, which also leads to a shallower sensing response under constant current bias than under constant field bias.

The proposed model, in addition to predicting the sensing response of the device, can also be used to track the optimal bias conditions of the material as well as to predict the trend of the ΔE effect of the material. The model can provide theoretical guidance for the performance tuning and variable stiffness design of devices such as sensors and energy harvesters based on the inverse effect of magnetostrictive materials.

For bundled conductors, the shadowing effect of upwind sub-conductor will affect the airflow and droplets distributions of downwind one, resulting in the difference in icing characteristics. Traditional icing calculation process generally ignored these differences and, hence, only giving an identical icing mass result of each sub-conductor. This affects the study of the aerodynamic characteristics and deicing methods of icing bundled conductor. Although some scholars have pointed out that the shadowing effect between sub-conductors will influence the icing process, there is no quantitative study. Therefore, this paper further explores the shadowing effect and relevant influencing factors of bundled conductors through numerical simulation and test research. Furthermore, based on the analysis of the shadowing effect and the superposition principle, a rapid calculation method of ice mass accreted on the bundled conductor is proposed.

Firstly, the distributions of airflow and droplets around bundled conductor are solved by Eulerian-Eulerian two-phase flow model. Secondly, combined with the mass and thermodynamic balance equations, the icing mass and shape accreted on bundled conductor under various icing environments are obtained. Then a new parameter called shadowing coefficient is defined to investigate the shadowing effect and influencing factors as well. The results show that: Shadowing effect is weakened with increasing absolute value of shadowing angle and bundled-spacing, but intensified with the increase of median volume diameter (M_VD) of droplets; Meanwhile, the shadowing effect experiences a growth and then drops down along with the increase of wind speed, and reach to the max at 15 m/s range 5~20 m/s.

Based on the superposition principle and shadowing effect analysis, a rapid calculating method for ice mass on bundled conductor is proposed. Where iced bundled conductor is regarded as a linear combination of non-shadowed sub-conductor (single conductor) icing intensity and shadowing coefficient, so the icing intensity of various types of bundled conductor can be obtained only requiring the icing intensity of single conductor and the shadowing coefficient in the corresponding environment. Then the rapid icing calculation formulars of 3,4,6,8-bundled conductor under various shadowing angle is given by geometry analysis, respectively, which simplifies the calculation of the icing mass on bundled conductor.

Finaly, a 4-bundled conductor nature icing test was carried out at the Xuefeng Mountain Energy Equipment Safety National Observation and Research Station to validate the accuracy of the numerical simulation and rapid calculation method. Results show that under the environment parameters of ambit temperature T_f = -2℃, M_VD = 25.4 μm, liquid water content L_wc = 0.61 g/m³, wind speed V = 10 m/s and shadowing angle θ = 2°, the difference in icing intensity between rapid calculation and test results was within -4.01% to -19.77%, the icing thickness differences of sub-conductors were between 1.66% to -6.36% and the differences in shadowing coefficient were between 4.05% to 5.33%, which well verifies the accuracy of the rapid calculation method proposed in this paper.

AC/DC hybrid system has become an effective solution for large-scale new energy consumption because of its characteristics of multiple power sources, multiple drop points, large capacity and cross-regional flexible transmission. However, the wide-frequency oscillation problem of AC/DC hybrid power system with high proportion of power electronics is prominent, which threatens the safe and stable operation of the system. At present, the stability analysis of hybrid system is mainly faced with the problem of how to take into account the AC and DC sections of the hybrid system at the same time and how to cover the system equipment with different impedance characteristics, so as to realize the unified analysis of the AC/DC hybrid network with multiple power electronic equipment. To address the above issues, this paper establishes a unified immittance network model for system-level stability assessment of hybrid systems.

First, based on the voltage/current source type device characteristics of the network devices, the impedance/ admittance forms of each network module of the system are standardized to avoid the problem of solving for the right half-plane poles in the process of system stability analysis. Secondly, from the perspective of the AC and DC ports of the system equipment, the AC/DC hybrid system can be divided into mono immittance subsystem and hybrid immittance subsystem. Finally, based on the interaction relationship between system immittance networks, a unified immittance network model containing complete oscillation information of the hybrid system is established. Under the premise of covering the stability information at each AC and DC port of the hybrid system, the unified immittance network model reduces the dimension of the system network model, at the same time expands the system network matrix from a single AC or DC system to an AC/DC system. Furthermore, combined with the derivation and change process of the immittance network, and through the modularization of the system expansion, this paper makes the immittance network applicable to the analysis of objects with different topologies,and extends the unified immittance network to general AC/DC hybrid systems. In addition,in contrast to the matrix model that merely aggregates a single impedance feature or a single admittance feature, the unified immittance network encompasses the AC/DC systems with various impedance characteristics, accomplishing the mutual unification of the new energy unit with admittance properties and the load network with impedance properties. This facilitates the precise establishment of the network mathematical model when confronted with the AC/DC system that concurrently incorporates equipment with current/voltage source characteristics, and realizes the unified coverage of equipment models with dissimilar impedance characteristics.

Based on the immittance network model, a unified immittance network stability criterion covering the interaction relationship of each AC/DC port of the hybrid system is derived. Combined with the expansion analysis of the immittance network, the criterion is extended to the general AC/DC hybrid system. The stability criterion comprehensively covers the stability problems of the AC/DC system, and avoids solving the problem of poles in the right half-plane, simplifying the analysis process. Furthermore, an oscillation traceability method for the hybrid system is given based on the immittance network model. Finally, based on the application examples of the AC/DC hybrid system, the immittance network model and its stability analysis method proposed in this paper are evaluated and verified.

Electric vehicles (EV) have the characteristics of both traffic and mobile load, and their charging behavior will have an interactive impact on the power grid. With the rapid increase in the number of electric vehicle and the continuous improvement of their penetration rate, charging guidance for large-scale EVs has become an important measure to alleviate the contradiction between local limited charging resources and strong charging demand. Therefore, considering the influence of future traffic information changes on navigation strategy, this paper proposes a fast guidance strategy for electric vehicle charging based on dynamic traffic inference.

First of all, a dynamic traffic information prediction model based on spatio-temporal self-supervised learning (ST-SSL) is established. A self-supervised learning (SSL) module for spatial and temporal heterogeneity of traffic data is designed to achieve accurate prediction of multi-period traffic flow information. Secondly, a multi-time dynamic impedance modeling method for urban road network considering future traffic information changes is designed, a charging navigation strategy considering multi-demand scenarios and multi-navigation objectives of users is established, and a solution method based on dynamic Dijkstra algorithm is proposed to realize the selection of the optimal charging station and the planning of the optimal navigation path. Based on the global charging navigation results, the service range of urban charging stations is dynamically evaluated, to achieve rapid charging guidance for electric vehicles. Finally, taking the actual road network of a certain area in Los Angeles as an example, the accuracy of the prediction model and the effectiveness of the guidance strategy are proved, which can effectively perceive the dynamic traffic information and quickly realize the service range division of urban charging stations and the charging guidance for electric vehicles.

In this paper, a fast guidance strategy for electric vehicle charging based on dynamic traffic inference is proposed, based on the case simulation results, the main conclusions can be obtained as follows. (1) The model based on ST-SSL can make full use of the spatial and temporal heterogeneity of traffic data, improve the prediction effect of traffic flow information, and provide an effective data basis for the construction of dynamic traffic impedance. (2) The proposed multi-scenario and multi-objective charging navigation strategy based on dynamic impedance can effectively perceive traffic information and take into account the diversified needs of users, effectively reduce the cost of charging navigation for different users, and reasonably guide the load distribution of electric vehicles. (3) The proposed dynamic Dijkstra algorithm can recommend the optimal path according to the future traffic information, which can be used as a navigation algorithm to plan the driving path, and can also recommend the customized optimal charging station according to the needs of users. (4) The division of charging station service range based on the global charging navigation results can effectively evaluate the service range of charging station, and provide an important reference for the construction planning of charging station. Based on the evaluation results, the charging navigation strategy is quickly assigned to each node, which effectively reduces the computing resource consumption of charging navigation.

Insulator is one of the most common and widely used electrical components in transmission lines, which plays a critical role in electrical insulation and mechanical support, ensuring that the current flows along the specified path and reducing electromagnetic interference with the surrounding environment. Since insulators are installed outdoors, they are exposed to wind, sunlight, rain, ice, frost and other bad weather for a long time, and their surface defects are inevitable. If the insulator appears self-explosion or drop string, which will cause leakage due to the loss of insulation, leading to electric shock accidents, thus resulting in huge economic losses. Relying on computer vision and deep learning technology, insulator defect detection from massive UAV aerial images has become an urgent problem for power operation and maintenance. However, the backgrounds of aerial images from overhead transmission line corridors are complex. Under different lighting conditions, shooting angles, shooting distances, etc., the scale of insulators in aerial images varies greatly, and the insulator strings are prone to occlusion, the defect area of the insulator is much smaller than the insulator itself. Therefore, there are numerous difficulties in detecting insulator defects in practical applications.

In recent years, compared with the traditional object detection methods, deep learning methods can quickly and accurately identify insulators and their defects from power inspection images. There is still a lack of comprehensive review of the latest progress in insulator defect detection in existing literature, without introducing object detection algorithms such as anchor free algorithm, YOLOv7, Transformer, and knowledge extraction techniques. In view of this, this article summarizes and analyzes a large number of visual methods for insulator defects detection, systematically reviews deep learning methods for insulator defect detection in drone aerial images, aiming to select appropriate detection methods for specific insulator defects and provide valuable reference for researchers engaged in transmission lines fault diagnosis.

This paper reviews the research progress of deep learning methods for insulator defect detection in UAV aerial images. Firstly, the current research status of transmission lines inspection based on deep learning is briefly reviewed. Then, the insulator defect detection methods based on deep learning are explained, mainly from the target detection models, lightweight network models, cascade detection models and other methods are summarized, which is conducive to the comparison between different deep learning methods and more helpful for power inspection personnel to select appropriate deep vision detection methods for fault diagnosis of specific electrical component. And the target detection models based on two-stage algorithms, one-stage algorithms and anchor-free algorithms are elucidated. The lightweight network models based on model pruning, knowledge distillation, low-rank decomposition, network quantization and the target detection model based on Transformer are summarized. Next, the self-built and public datasets for insulator defect detection are introduced. Due to the lack of training samples and unified dataset for insulator defect detection, scholars mostly conduct defect detection research through self-built datasets in different detection scenarios. Finally, the challenges faced by insulator defect detection methods based on deep learning are elucidated, including insufficient defect samples, low defect detection accuracy, difficulty in detecting small target defects, and feature extraction, etc. Based on existing deep learning techniques and recent research ideas, several important research directions in the future are pointed out, including expanding insulator defect samples, establishing unified performance evaluation indicators, small and zero sample learning, new defect detection frameworks, multi-level detection of small defects, deep fusion of multiple learning technologies, cloud-edge-end collaborative fusion, and improving network model stability and real-time performance.

In substation robot inspection tasks, high-precision semantic segmentation of 3D point cloud data is one of the key technologies. Traditional point cloud semantic segmentation algorithms have certain limitations, making it difficult to handle complex 3D scenes. Deep learning methods have compensated for the shortcomings of traditional point cloud semantic segmentation algorithms and have become the main method for achieving point cloud semantic segmentation. However, existing point cloud segmentation methods for substations face issues such as high complexity, low accuracy, and gradient vanishing. To address these issues and achieve accurate segmentation of the main equipment point cloud in substations, this paper proposes a high-precision semantic segmentation method for substation main equipment point clouds based on DI-PointNet.

Firstly, on the basis of the PointNet++ network structure, a double-layer consecutive transformer (DLCTransformer) module is introduced. Key points are sampled through the DLCTransformer to enhance information interaction between point clouds and expand the effective receptive field. Secondly, a hierarchical key sampling strategy is adopted. The point cloud data is divided into the original dense point cloud space and a sparse point cloud space formed after farthest point sampling. These are then divided into multiple non-overlapping 3D windows, ultimately generating key values required for self-attention mechanism calculations, thereby reducing computational complexity, improving the model’s receptive field, and aggregating long-range context to achieve information interaction of substation-associated point clouds. Finally, an inverted residual module (InvResMLP) based on residual connections and inverted bottleneck design is added to the network. This enhances the model’s ability to extract complex structural features from substation point clouds while effectively reducing the gradient vanishing problem, making the algorithm more robust in handling complex substation scenarios and improving the accuracy of semantic segmentation of substation main equipment point clouds.

Additionally, to validate the segmentation effectiveness of the algorithm, this paper uses Avia LiDAR equipment to collect point cloud images of different devices at substations such as the Baobei substation in Baoding City. The original data includes transformers, switchgear, steel towers, insulators, maintenance equipment, and others (mainly vegetation and buildings). To simplify the point cloud data while filtering noise, the original input point cloud is first subjected to grid sampling with a grid size of 0.03 m. Data augmentation methods such as z-axis rotation, scaling, perturbation, and color reduction are employed. The initial window size is set to 0.12 m and is doubled after each down-sampling layer. The DI-PointNet is trained using the cross-entropy loss function and Adam optimizer with the following hyperparameters: initial learning rate of 0.001, batch size of 2, and 100 epochs. To ensure the reasonableness and accuracy of the experiments, the comparative algorithms used in this paper are trained using the same hardware platform, environment version, loss function, optimizer, hyperparameters, and training strategies as DI-PointNet.

Through ablation experiments and comparative analysis, the DI-PointNet algorithm proposed in this paper improves the overall accuracy (OA) value of substation point cloud segmentation by 3.4 percentage points compared to before the improvement, while reducing algorithm complexity. The proposed algorithm outperforms other mainstream deep learning algorithms and other point cloud segmentation algorithms in the power sector. The performance of this algorithm is close to the accuracy of manual segmentation and can achieve precise segmentation of substation point clouds.

The increasing distance of offshore wind farms from coastal areas has created an urgent need for the development of long-term extra high voltage direct current (EHVDC) cables. Factory joints are commonly used to connect sections of submarine cables, forming extensive cable systems. Therefore, studying factory joint is crucial for advancing long-length cable lines. This study investigates the physicochemical and dielectric insulation characteristics of XLPE samples under various vulcanization pressures, highlighting the effects of these pressure changes on the properties of 500 kV EHVDC cross-linked polyethylene (XLPE) cable joints.

Commercially available 500 kV EHVDC XLPE pellets were used to prepare the XLPE samples via hot-press method. Initially, a specified quantity of XLPE pellets was distributed between two iron plates. The pellets were preheated at 120℃ for 5 minutes and then heated at 180℃. Cross-linking was subsequently performed under different vulcanization pressures of 1.3 MPa, 1.6 MPa, 1.9 MPa and 2.5 MPa respectively. The fabricated XLPE specimens underwent physical characterization through Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), X-ray diffraction (XRD), and gel content analysis. While electrical measurements included current density analysis, pulsed electro-acoustic (PEA) analysis, and DC breakdown test.

The physiochemical results indicate that increasing vulcanization pressure enhances the crosslinking degree of XLPE samples, transforming the material from a linear molecular structure to a 3D network structure and breaking macromolecules into smaller, mobile molecules. The increased mobility of these small molecules leads to improved crystallinity, resulting in a higher crystallinity structure. Additionally, the recrystallized macromolecular chains have higher melting temperatures, raising the overall melting temperature of the samples. However, higher vulcanization pressure also produces crosslinking by-products that are difficult to decompose and volatilize. The combination of high temperatures and pressures causes thermal expansion forces perpendicular to the lamellae, increasing lamella spacing, creating more amorphous regions, and effecting the insulation performance of the samples.

Regarding electric insulation performance, the DC breakdown strength and space charge injection threshold strength of the fabricated XLPE samples initially increase and then decrease with the increase in vulcanization pressure. Conversely, conductivity current and average space charge density first decrease and then increase. An optimal vulcanization pressure of 1.9 MPa was identified, at which the XLPE samples exhibited improved electrical insulation properties. Below this pressure, the increased trap energy levels inhibit carrier transport, thereby reducing the number of free carrier paths and hindering the formation of conductive channels, ultimately increasing the breakdown strength of the XLPE samples. However, at vulcanization pressures above 1.9 MPa, the increased crosslinking byproducts create more shallow traps, which lower space charge injection and accumulation, ultimately distorting the sample's internal electric field. Additionally, the increased lamella spacing creates more amorphous regions, reducing the carrier transport barrier and further decrease the breakdown strength of the prepared XLPE samples.

Based on the results, it can be concluded that appropriately increasing the vulcanization pressure of factory joints improves the physicochemical and electrical properties of XLPE. However, excessively high vulcanization pressure can have a detrimental impact on the electrical insulation properties of cable factory joints.

Epoxy resin (EP) possesses advantages such as low cost, high mechanical strength, robust chemical resistance, and excellent electrical insulation properties, making it extensively utilized in various epoxy cast electrical equipment like dry transformers and reactors. Nonetheless, the three-dimensional cross-linked network of resins exhibits non-melting characteristics, posing challenges in the degradation and recycling of retired epoxy electrical equipment. Nowadays, researchers have achieved epoxy resin recycling by incorporating dynamic covalent bonds into the epoxy resin crosslinking network to develop degradable Vitrimer epoxy resin materials. However, when applied to complex environments like high temperature, humidity, and intense electric fields, the internal crosslinking network of epoxy Vitrimers material may deteriorate, impacting its operational longevity. Therefore, besides ensuring favorable electrical, mechanical, and thermal properties, the enduring reliable performance of Vitrimer resin cannot be disregarded. This paper prepared dual-dynamic bonds Vitrimer resin with varying disulfide bond contents. The micromorphology, electrical characteristics, mechanical properties, dynamic thermodynamic properties, and degradation properties of Vitrimer resin at diverse aging stages were regularly investigated and the life evaluation model is constructed at last.

Firstly, dual dynamic crosslinked Vitrimer resin basded on ester bonds and varied disulfide bonds were prepared with 3,3’-dithiodipropionic acid (DTDPA) and hexahydro-4-methylphthalic anhydride (MHHPA) as the curing agent, with Triethanolamine acting as the catalyst. Subsequently, the accelerated thermo-oxygen aging tests were carried out, during which the microscopic morphology, electrical properties, bending characteristics, dynamic thermodynamic attributes, and degradation properties of vitrimer resin were periodically evaluated. Experiment results revealed alterations in the resin's microstructure under hot oxygen aging, leading to random internal cross-linked network fractures that generate abundant free radicals, ultimately causing resin failure. The resin's bending strength diminishes, rigidity increases, toughness notably decreases, and the bending fracture transitions to a brittle fracture pattern. As aging progresses, a denser cross-linked network forms on the resin's surface, elevating T_g. The integration of disulfide bonds makes the resin system more susceptible to oxidation and molecular chain breakage, resulting in reduced breakdown voltage, heightened dielectric loss factor, and increased insulation deterioration. Throughout the aging process, the degradation rate of Vitrimer resin in glycol solution decreases due to surface ester bond reduction and oxide layer formation, while the destruction of the disulfide crosslinking network prevents resin degradation in dithiothreitol solution. Lastly, a life evaluation model for the dual dynamic crosslinked Vitrimer resin was formulated based on the results of bending strength and TGA tests.

The dual dynamic crosslinked Vitrimer resin has excellent comprehensive properties and can realize the recycling of decommissioned epoxy electrical equipment. In this paper, the effect of thermal oxygen aging on the properties of dual dynamic crosslinked degradable resin was studied, which laid the experimental and theoretical foundation for the long-term service of vitrification epoxy resin in electrical equipment.

Metal oxide surge arresters are crucial for overvoltage protection in power systems, determining the insulation level of electrical equipment, with their core component being the ZnO varistor. However, modern stable ZnO varistors exhibit an anomalous decrease in power loss during aging, contradicting the increase in power loss predicted by the classical ion migration model. This discrepancy poses challenges for the condition assessment and life prediction of ZnO varistors due to a lack of theoretical foundations, thereby presenting a potential threat to the power system. Consequently, the study of the anomalous aging mechanism of stable ZnO varistors has been identified as a major challenge for the varistor community by CIGRE in both 2013 and 2017.

In this paper, stable ZnO varistors are subjected to accelerated DC aging at elevated aging temperatures to investigate their long-term stability transition. With increase in aging temperature, power loss trend transitions from a continuous decrease at 120℃ to an initial decrease followed by an increase at 150℃, and a sustained rise at 180℃. The decreasing power loss trend can be fitted by a double exponential decay function, while the increasing power loss is proportional to the square root of the aging time t^0.5. After transitioning to a mixed stable type at 150℃, the aging of stable ZnO varistors becomes irreversible. In-situ high-temperature dielectric measurements reveal that the interface space charge polarization relaxation process shifts to higher frequencies with decreased relaxation time and activation energy decreasing from 0.583 eV to 0.560 eV, indicating the destruction of the grain boundary structure. Low-temperature dielectric tests show that intrinsic point defects of zinc interstitials undergo irreversible consumption after aging. Upon transitioning to an instable type at 180℃, the "crossover" phenomenon of the forward current-voltage (I-U) characteristics disappears at 180℃, and both forward and reverse I-V characteristics shift towards increased leakage current region as a whole. Severe deterioration in reverse electrical parameters was observed, as breakdown voltage U_1mA decreases from 200.5 V to 92.9 V, the nonlinear coefficient α decreases from 16.3 to 2.0, and the leakage current rises from 19.5 μA to 479.3 μA. More importantly, offline physical and chemical structural tests show a reduction in the diffraction angles of ZnO crystal planes and decreased peak intensities. Additionally, a significant decrease in the binding energy of the Zn_2p orbital is observed, with Zn_2p3/2 and Zn_2p1/2 orbitals decreasing from 1 022.5 eV and 1 045.9 eV to 1 022.1 eV and 1 045.2 eV, respectively. This indicates the reduction of zinc interstitials and confirming that the interface states cannot maintain stability at high temperatures, thus becoming neutralized and consumed with zinc interstitials.

These findings demonstrate that the essence of the decreasing power loss in stable ZnO varistors lies in the stable interface states at the grain boundary, which, however, cannot maintain stable at certain high temperatures. The interface states would then neutralize with the zinc interstitials due ion migration, subsequently leading to the reduction of zinc interstitials and the destruction of the ZnO lattice, resulting in significant deterioration of ZnO varistors. Therefore, optimizing the high-temperature stability of the interface states is crucial for enhancing the long-term stability of ZnO varistors.

With global climate warming, the frequency of heavy rainstorm events is increasing. During heavy rainstorms, composite suspension insulator strings are prone to reduced creepage distance utilization and potential rain flashover accidents due to the sudden increase in rain intensity and rain columns bridging the skirt gaps. Since the rod diameter and skirt diameter of composite suspension insulators are smaller than those of large-diameter composite pin insulators, and there are significant differences in skirt structure and creepage distance, the rain flashover characteristics of suspension insulators cannot be equated with pin insulators. Hence, there is insufficient data on the rain flashover characteristics of composite suspension insulators.

This paper conducts artificial rain flashover tests under strong rainfall conditions using five different skirt structures of composite insulators. It investigates the effects of rain intensity and water conductivity on insulator AC flashover characteristics by analyzing the rain column length at the skirt edge, simulation models, arc development paths, and critical leakage currents. A formula for calculating the unit insulator height AC flashover voltage under the combined effects of rain intensity and water conductivity is proposed and verified for accuracy.

Results indicate that the unit insulator height rain flashover voltage is negatively correlated with rain intensity and water conductivity, following an exponential decay function. The voltage gradient reduction can reach 41.9% and 43.8% respectively due to these factors. The rainfall intensity of 9 mm/min can be considered the tipping point between non-extreme and extreme rainfall. Additionally, composite insulators with larger skirt diameters and skirt spacings are more significantly affected by rain intensity, while the impact of water conductivity is consistent across different skirt parameters. When the rainfall intensity is low, the shorter rain column causes the arc to develop along the path of “rain column-air gap-small skirt surface”. When the rainfall intensity is high, the rain column is longer, and the vertical air gap within the rain column is directly broken down, leading to the arc development path of “rain column-vertical air gap-large skirt surface”.

In addition, the maximum length of the rain column at the skirt increases with rain intensity and varies with skirt diameter and spacing. The arc development path differs significantly among insulators with different skirt parameters. Insulators with larger skirt diameters and spacings exhibit a combined air gap and skirt surface discharge path, with reduced rain column bridging the skirt gap due to effective skirt coverage, resulting in higher creepage utilization. Therefore, insulators with larger skirt diameters and spacings can effectively enhance the insulation strength. Due to the influence of the number of charged particles in the water and the area covered by the water film, the average critical leakage current increases with rain intensity and water conductivity, which increases the energy gained by the electric arc and promotes the development of local arcs and the formation of discharge channels. During heavy rainstorms, to reduce surface leakage current on composite insulators and prevent rainwater from bridging the skirts to form a “rain pillar-vertical air gap” continuous arc path, it is recommended to use composite insulators with larger inter-skirt spacing and skirt diameters in regions prone to frequent heavy rainfall.