Abstract:
Precisely detecting the fault location on transmission lines can significantly save labor effort and accelerate the repairing and restoration process. This paper presents...Show MoreMetadata
Abstract:
Precisely detecting the fault location on transmission lines can significantly save labor effort and accelerate the repairing and restoration process. This paper presents a novel single-ended fault location approach for transmission lines using modern deep learning techniques. A mixed convolutional neural network with long short-term memory (LSTM) structure are trained to predict the fault distance given the single-ended voltage and current measurements. Convolutional function, pooling layers, and the LSTM structure are used 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. Extensive studies have demonstrated the accuracy and effectiveness of the deep-learning-based, singled-ended fault location approach.
Published in: 2019 North American Power Symposium (NAPS)
Date of Conference: 13-15 October 2019
Date Added to IEEE Xplore: 17 February 2020
ISBN Information: