Transmission Line Fault Detection & Phase Selection using ANN

Abstract

A novel application of neural network approach to protection of transmission line is demonstrated in this paper. Different system faults on a protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of neural networks as a protective relaying pattern classifier algorithm. The proposed method uses current signals to learn the hidden relationship in the input patterns. Using the proposed approach, fault detection, classification and faulted phase selection could be achieved within a quarter of cycle. An improved performance is experienced once the neural network is trained sufficiently and suitably, thus performing correctly when faced with different system parameters and conditions. Results of performance studies show that the proposed neural network- based module can improve the performance of conventional fault selection algorithms. In this paper, a new scheme is proposed for fast and reliable fault detection and phase selection. The proposed method uses an artificial neural network-based scheme. Various transient system faults are modeled and an ANN- based algorithm is used for recognition of these patterns. Performance of the proposed scheme is evaluated using various fault types and encouraging results are obtained. It is shown that the algorithm is able to perform fast and correctly for different combinations of fault conditions, e.g. fault type, fault resistance, fault inception angle, fault location, prefault power flow direction and system short circuit level.