A pixel-level assessment method of the aging status of silicone rubber insulators based on hyperspectral imaging technology and IPCA-SVM model

Acidic environments are a significant factor in the aging and failure of silicone rubber insulators. Addressing the effective assessment of insulators’ aging state to prevent power transmission accidents has been a critical and urgent issue for the power grid. Therefore, hyperspectral imaging (HSI) technology was employed in this paper, capturing spectral line data of silicone rubber in six aging states in both visible and near-infrared regions, respectively. To reduce data redundancy, genetic algorithm (GA) and band weighting were introduced to improve traditional principal component analysis (PCA), with performance compared using overall accuracy (OA) and Kappa, against 12 other feature extraction or dimensionality reduction methods. The improved principal component analysis − support vector machine (IPCA-SVM) model proposed effectively minimizes irrelevant information in hyperspectral original data, exceeding 93% accuracy and improving OA by 8.26% compared to all bands data. Finally, the IPCA-SVM model was used for pixel-level assessment of the surface aging state of silicone rubber insulators, demonstrating its reliability. This method effectively characterizes the aging state of composite insulators, providing a solid foundation for the safe and stable operation of power grids.