Transmission Line Fault Location Using Deep Learning Techniques

Abstract

This letter presents a novel single-ended fault location approach for transmission lines that uses modern deep learning methods. A mixed convolutional neural network and long short-term memory structure are proposed to preserve the translation invariance and capture the temporal correlation of the time-series input data. Advanced deep learning techniques such as adaptive moment estimation and dropout are used to efficiently train the neural network and prevent over-fitting. Our extensive studies have demonstrated the accuracy and effectiveness of the deep-learning-based, singled-ended fault location approach.