Fault Classification and Localization in Power Transmission Lines Using LSTM and Vibration Data Analysis

Abstract

The precise classification and localization of faults in power transmission lines are very important to ensure grid stability and to minimize service interruptions. Traditional methods of fault detection frequently have difficulty with complicated fault scenarios, faults of high impedance, and varying conditions of operation. Throughout this paper, an approach of a new machine learning-based for fault classification and localization in power transmission lines adopting Long Short-Term Memory (LSTM) networks and vibration data analysis is proposed. In contrast with conventional impedance-based and methods of traveling wave, the proposed approach takes advantage of temporal dependencies within vibration signals for enhancing predictive precision. Through capturing different faults types, simulation as well as location, a dataset of fault conditions is being generated. For recognizing distinguishing fault patterns and predicting location’s fault with high accuracy, LSTM-based model we proposed is trained. The experimental results show how the LSTM model is superior in dealing with sequential data and, subsequently, improves fault localization precision. Moreover, experimental outcomes confirm that the proposed approach is robust, which leads to high classification precision and minimum localization error. The findings show the potentiality of vibration-based machine learning models to revolutionize fault management within power grids, which offers a solution that is more adaptive and data-driven to the challenges of fault classification and localization.