Khan Umer Amir, Asif Mansoor, Zafar Muhammad Hamza, Alhems Luai
Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
Interdisciplinary Research Center for Sustainable Energy Systems, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
Sci Rep. 2025 Apr 17;15(1):13246. doi: 10.1038/s41598-025-97646-4.
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.
本文对利用泄漏电流进行污染高压绝缘子污染分类的机器学习进行了全面的实验验证。在可控的实验室条件下，建立了一个关于不同污染水平瓷绝缘子泄漏电流的详细数据集。数据集中还包括温度和变化湿度等关键参数，以反映环境条件的影响，并使数据集接近实际场景。对生成的数据集进行预处理，并从时间、频率和时频域中提取关键特征。在此数据集上训练和评估了四种不同的机器学习模型，包括决策树和神经网络。采用贝叶斯优化技术对机器学习模型的参数进行优化。这些模型表现出卓越的性能，准确率始终超过98%。值得注意的是，与神经网络模型相比，基于决策树的模型训练和优化时间明显更快。本研究强调了机器学习在提高绝缘子维护和监测系统可靠性方面的有效性，为更强大的预测性维护策略铺平了道路。
