Experimental validation of machine learning for contamination classification of polluted high voltage insulators using leakage current.

This paper presents a comprehensive experimental validation of machine learning for contamination classification of polluted high voltage insulators using leakage current. A meticulous dataset of leakage current for porcelain insulators with varying pollution levels was developed under controlled laboratory conditions. Critical parameters of temperature and varying humidity were also included in the dataset to reflect the impact of environmental conditions and bring the dataset close to real world scenarios. The dataset generated was preprocessed and critical features were extracted from time, frequency, and time-frequency domains. Four distinct machine learning models, encompassing decision trees and neural networks, were trained and evaluated on this dataset. The Bayesian optimization technique was used to optimize the parameters of Machine Learning Models. The models demonstrated exceptional performance, with accuracies consistently exceeding 98 %. Notably, the decision tree-based models exhibited significantly faster training and optimization times compared to their neural network counterparts. This study underscores the effectiveness of machine learning in improving the reliability of insulator maintenance and monitoring systems, paving the way for more robust predictive maintenance strategies.