High temperature vulcanised silicone rubber (SR) has excellent hydrophobicity and thus is widely applied both in indoor and outdoor insulation. According to the analysis of different experimental ageing tests and field investigation of SR, temperature and humidity are considered to be the fundamental factors during degradation. Hygrothermal ageing was proposed in this study to simulate the chalking state of the SR, finally leading to the irreversible degradation far below the thermal decomposition temperature. The variation of morphology, hardness and polydimethylsiloxane (PDMS) content was quantified during the yearlong hygrothermal ageing test. It is implied that the gradual process of whitening and hardening are accompanied by the decline of the PDMS content. Finally, ageing delamination in SR and its degradation mechanism were proposed.

In the last decade, the decay-like aging phenomena of fiber reinforced plastic (FRP) core rods in composite insulators appeared frequently, and have significantly affected the safety of power transmission lines. However, the mechanism of its occurrence and development is still unclear. In this work, a method based on rod-housing interfacial discharge is proposed to artificially reproduce the occurrence and development of decay-like aging in FRP core rods under laboratory conditions. The aging process is explored under a high-humidity environment, where high-acidity and high-temperature conditions are also provided by interfacial discharge. The micro- and macro-scopic structures, physicochemical properties and leakage currents of FRP samples are characterized during a 1000-hour aging process. The laboratory aged samples resemble the decay-like phenomena in on-site insulators, indicating the effectiveness of this method. Experimental results show that interfacial discharge can trigger the decomposition of epoxy resin in FRP core rods, leading to exposure of glass fibers. Electrical erosion slows down as aging time increases, and the erosion rate is positively related to the porosity of core rods. Further discussion indicates that erosion is impeded by glass fibers in the surface aging layer due to its high thermal and chemical stability, and a three-stage erosion process model is proposed. Finally, a semi-empirical model is proposed to describe this aging process, in which the influence of core rod porosity is taken into account. This work provides an effective laboratorial method to reproduce the decay-like aging of composite insulators, and reveals its erosion process and the underlying mechanism.

Accumulating a substantial amount of textual data on power equipment defects during maintenance and inspection stages presents a valuable problem of assessing and grading these text-based information. This paper proposes a dual-channel text feature extraction model based on the pre-trained BERT model, applied to the evaluation of power equipment defect levels in textual data. Firstly, a dataset of power equipment defect levels is established, followed by data augmentation and preprocessing. Then, a neural network model is constructed, utilizing the pre-trained BERT model for initial semantic information extraction from the text, further extracting features through two modules, Bi-LSTM and CNN, on top of BERT’s output. Finally, the obtained feature vectors are concatenated to generate the output. Comparative experiments with other algorithms demonstrate that the proposed method out-performs others in multiple metrics, achieving an F1 score of 96%. The research findings can serve as a reference for achieving intelligent processing of power textual information.

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 discharge and flashover phenomenon of post insulators in rainy weather has not been given sufficient consideration; however, with the construction of ultra-high voltage power grids, the performance of the external insulation and the ability to withstand special climate conditions need to be guaranteed. Therefore, it is meaningful to conduct studies on the discharge characteristics of contaminated post insulators under rainfall conditions. Moreover, the conventional perception tends to confuse the flashover of polluted insulators in the rain with the pollution flashover that occurs in the fog; however, in fact, the discharge of contaminated insulators that occurs during rainfall has characteristics that can be distinguished from the pollution flashover. In this study, firstly, the current status of research on the external insulation characteristics of post insulators was analyzed through an examination of the available literature. Secondly, the concept of a ‘pollution rain flashover’ of insulators was established and clarified, to distinguish it from the traditional meaning of ‘pollution flashover’ or ‘contamination flashover’. Thirdly, research results on the pollution rain flashover of post insulators used in power stations in recent years were summarized, which included the characteristics and mechanism of the discharge, parameters and factors influencing the flashover voltage, and their influence laws. Particularly, the gap discharge between insulator sheds triggered by raindrops, which is the most significant feature of the pollution rain flashover, and the profile optimization of sheds, which is an effective way to improve the performance, were emphasized in this work. Fourthly, the prevention methods were studied, which mainly include the application of rainproof sheds and the shed optimization for pollution rain flashover of post insulators. Finally, a brief prospect is given for future research.

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.