Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. (June 2021) - Belagoune, Soufiane; Bali, Noureddine; Bakdi, Azzeddine; Baadji, Bousaadia; Atif, Karim
Highlights: Intelligent protection against transmission line faults in large-scale multi-machine power systems. Deep Learning classification and regression models are developed through Recurrent Networks. Three LSTM models enable sequence learning from PMU-measured current and voltage patterns. The models ensure accurate fault region identification; all-type classification; and location prediction. Extensive data validation across numerous scenarios in a Two-Area Four-Machine Power System. Abstract: Fault detection, diagnosis, identification and location are crucial to improve the sensitivity and reliability of system protection. This maintains power systems continuous proper operation; however, it is challenging in large-scale multi-machine power systems. This paper introduces three novel Deep Learning (DL) classification and regression models based on Deep Recurrent Neural Networks (DRNN) for Fault Region Identification (FRI), Fault Type Classification (FTC), and Fault Location Prediction (FLP). These novel models explore full transient data from pre- and post-fault cycles to make reliable decisions; whereas current and voltage signals are measured through Phasor Measurement Units (PMUs) at different terminals and used as input features to the DRNN models. Sequential Deep Learning (SDL) is employed herein through Long Short-Term Memory (LSTM) to model spatiotemporal sequences of high-dimensional multivariate features to achieve accurate classification and prediction results. The proposed algorithms were tested in a Two-Area Four-Machine Power System. Training and testing data are collected during transmission lines faults of different types introduced at various locations in different regions. The presented algorithms achieved superior detection, classification and location performance with high accuracy and robustness compared to contemporary techniques.