Application of Principal Component Analysis for Fault Classification in Transmission Line with Ratio-Based Method and Probabilistic Neural Network: A Comparative Analysis

Abstract

The proposed work illustrates a simple research approach to identify the type of fault in a three-phase overhead single-end-fed long transmission line. Multivariate statistical methods like principal component analysis (PCA) alone, and in combination with probabilistic neural network (PNN), have been applied here to classify fault. An attempt has been made to use the PCA features obtained from the analysis of electrical parameters for each of the faults, in two ways. The first approach of fault classification is based on analyzing the PCA features by a modified ratio-based analysis. In the second method, an attempt has been made to use the PCA features directly to a structured PNN model. Electromagnetic Transient Program simulation software has been used to simulate a transmission line model. Sending-end three-phase line currents corresponding to various faults carried out at different geometric distances along the transmission line have been analyzed in MATLAB environment. The proposed algorithms are tested with unknown and intermediate distant faults with variable fault resistance to validate the same. Finally, a comparative analysis of the proposed two methods is illustrated, which shows 100% classifier accuracy of both the models.