Robust Ensemble Framework for Fault Detection in Transmission Lines Using Hybrid Classifiers and Deep Q-Networks

Abstract

The continuous complexities and the requirement of reliable power transmission have led to the necessity for advanced fault detection systems in order to ensure stability and safety in electrical grids. In this study, an ensemble learning-based technique has been proposed for fault detection in power transmission lines by using Deep Q-Networks (DQN) in conjunction with standard classifiers such as Naive Bayes, Multilayer Perceptron (MLP), Logistic Regression, and Deep Forest. The proposed algorithm uses reinforcement learning for optimizing classifier weights. Voltage and current waveforms are used as input data for extracting features and performing classification. The performance of the proposed technique is highly improved, providing 99.10% accuracy, 99.40% precision, 99.70% sensitivity, and 99.50% F1-score, which is far better than the existing techniques. Moreover, the proposed framework provides reduced processing time (12-13 milliseconds) and low memory consumption (150-152 megabytes).