Accurate fault location and faulted section determination based on deep learning for a parallel‐compensated three‐terminal transmission line

Abstract

Parallel flexible AC transmission systems (FACTS) devices affect the performance of protection relays and conventional phasor‐based fault location schemes in transmission lines. This study focuses on both multi‐terminal and parallel‐compensated lines, not investigated simultaneously in previous works. An algorithm based on deep neural networks is proposed for fault location in a three‐terminal transmission line with the presence of parallel FACTS device. The line model and fault occurrence are simulated in SIMULINK and features are extracted from voltages at the three terminals by wavelet transform. The generated features are used to train a deep neural network which determines faulted line section and fault distance simultaneously. The adopted intelligence‐based approach has the advantage of not requiring pre‐knowledge of line specifications, FACTS devices modelling and the uncertainty in compensator parameters. A large number of fault scenarios are investigated. The faulted section is recognised correctly in 100% of test cases. The algorithm performance is acceptable for both symmetrical and unsymmetrical fault types, small fault inception angles and high fault resistance. The accuracy of fault location is improved compared to previous schemes (total mean error of 0.0993%). The proposed algorithm provides an accurate, fast and robust tool for fault location in parallel‐compensated three‐terminal transmission lines.