A SVM-based method for electric field feature extraction and insulation condition characterization of deteriorating insulators

This paper proposes a novel SVM-based method for electric field feature extraction and insulation condition characterization of deteriorating insulators. The method integrates wavelet transform for feature extraction and Support Vector Machine (SVM) for classification, providing a robust solution for insulator health monitoring. The proposed framework is designed to address the challenges posed by noisy, contaminated, and diverse environmental conditions in power grid infrastructure. Extensive experiments were conducted using both synthetic and real-world datasets to evaluate the performance of the model. The results demonstrate that the proposed method outperforms traditional feature extraction techniques and classification models in terms of accuracy, robustness, and scalability. The method achieved a classification accuracy of 92.5% on the real-world dataset, with strong generalization across different operational conditions. Additionally, the scalability of the approach was tested on large datasets, demonstrating its suitability for real-time monitoring in power distribution systems. This work contributes to the advancement of intelligent monitoring techniques for electrical grid maintenance and failure prevention.