The authors investigated the effect of the insulator hydrophilic fraction on the pollution flashover performance by the up‐and‐down method. The hydrophobicity of parts subjected to a high electric field intensity such as two ends of an insulator, may be weakened by corona discharge. In this study, they simulated the behaviour of composite insulator surfaces at six hydrophilic fractions. Furthermore, arc discharge was recorded using a high‐speed camera and the probability of arc discharge was analysed. They found that as the hydrophilic fraction was increased, the 50% probable flashover voltage first decreased and then increased. The wetting process and discharge type could be classified into two types and the flashover process could be divided into two stages by a clear hydrophilic fraction boundary through leakage current characteristic, discharge probability, and arc shape analysis. The results indicated that uneven hydrophobicity loss might lead to further flashover voltage reduction, up to 26.5% lower, than that with uniform hydrophilicity. This should be considered in transmission line design and maintenance of composite insulators.

With the widespread deployment of high-voltage and ultra-high-voltage transmission lines, composite insulators play a vital role in modern power systems. However, prolonged service leads to material aging, and the current lack of standardized, quantitative methods for evaluating silicone rubber degradation poses significant challenges for condition-based maintenance. To address this measurement gap, we propose a novel aging assessment framework that integrates Fourier Transform Infrared (FTIR) spectroscopy with a measurement-oriented ensemble learning model. FTIR is utilized to extract absorbance peak areas from multiple aging-sensitive functional groups, forming the basis for quantitative evaluation. This work establishes a measurement-driven framework for aging assessment, supported by information-theoretic feature selection to enhance spectral relevance. The dataset is augmented to 4847 samples using linear interpolation to improve generalization. The proposed model employs k-nearest neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosting Decision Tree (GBDT) within a two-tier ensemble architecture featuring dynamic weight allocation and a class-balanced weighted cross-entropy loss. The model achieves 96.17% accuracy and demonstrates strong robustness under noise and anomaly disturbances. SHAP analysis confirms the resistance to overfitting. This work provides a scalable and reliable method for assessing silicone rubber aging, contributing to the development of intelligent, data-driven diagnostic tools for electrical insulation systems.

The surface ageing of silicone rubber composite insulators, widely used in power systems, poses significant challenges. This study integrates Fourier transform infrared (FTIR) spectroscopy with machine learning to evaluate ageing states and explore underlying mechanisms under various environmental conditions. A dataset covering light, medium, and severe ageing was built through FTIR experiments, spectral feature extraction, and data augmentation. An ensemble learning model achieved a classification accuracy of 95.42%. SHapley Additive exPlanations (SHAP) analysis indicated that silicon–oxygen backbones, silylmethyl groups, and hydroxyl groups are key to the ageing process. The silicon–oxygen backbone is dominant in initial oxidation and cross‐linking, whereas silylmethyl group reactions occur later. Hydroxyl group changes are complex and strongly environment‐dependent during severe ageing. The model was also applied to naturally aged samples from Tibet and Inner Mongolia, showing strong classification performance and revealing clear regional differences. These findings are valuable for assessing surface ageing, analysing ageing mechanisms and developing grading standards for composite insulators.

High-voltage transmission and substation projects at high altitudes are pivotal in realizing the objective of universal electricity access. However, the reduced air density at elevated heights facilitates the formation and propagation of discharges, posing more stringent challenges to the external insulation of these projects compared to their counterparts in plains areas. Furthermore, considering the influence of meteorological conditions such as rainfall, it is imperative to conduct comprehensive experimental studies on the insulation properties of air gaps to inform the design and maintenance of engineered external insulation. This paper presents the results of rod–plane gap discharge tests conducted under dripping conditions at an actual high-altitude location of 2500 m. The employed test methodology effectively simulates the impact of rainfall on the insulation characteristics of the gap. Based on the experimental findings, a detailed analysis is conducted on the effects of gap distance, dripping flow rate, and conductivity on the gap breakdown voltage. Additionally, the discharge paths and underlying mechanisms under water-dripping conditions on rod electrodes are briefly discussed. The acquired data and conclusions contribute to a deeper understanding of the mechanisms governing rainfall effects on gap discharges and provide valuable insights for the design of external insulation in high-altitude HVDC transmission projects.

Composite insulators are critical components in power systems, and the accurate assessment of their aging state is essential for ensuring the safe operation of the power grid. In this study, a large amount of Fourier-transform infrared (FTIR) spectra for composite insulators is collected to establish an aging classification dataset. A deep learning model based on a one-dimensional convolutional neural network (1D-CNN), termed FTIR Aging Net (FAN), is proposed. This model effectively leverages the entire spectral range of FTIR to accurately classify the aging state of composite insulators, achieving a classification accuracy of 98.44%. This high level of accuracy underscores the model’s ability to capture discriminative features from the FTIR for reliable aging assessment. An interpretability analysis of the model reveals that, during the early stages of aging, the primary degradation occurs in the Si-O backbone and C=C bonds, while in the later stages, further breakage of C-H bonds is observed. FAN facilitates the assessment of composite insulator aging, providing valuable insights into the aging mechanisms of the material. This can serve as a technical support tool for the routine maintenance and operation of composite insulators.