Power system fault identification and localization using multiple linear regression of principal component distance indices - Alok Mukherjee; Palash Kr. Kundu; Arabinda Das (Institute of Advanced Engineering and Science, 2020)
This paper is focused on the application of principal component analysis (PCA) to classify and localize power system faults in a three phase, radial, long transmission line using receiving end line currents taken almost at the midpoint of the line length. The PCA scores are analyzed to compute principal component distance index (PCDI) which is further analyzed using a ratio based analysis to develop ratio index matrix (R) and ratio error matrix (RE) and ratio error index (REI) which are used to develop a fault classifier, which produces a 100% correct prediction. The later part of the paper deals with the development of a fault localizer using the same PCDI corresponding to six intermediate training locations, which are analyzed with tool like Multiple Linear Regression (MLR) in order to predict the fault location with significantly high accuracy of only 87 m for a 150 km long radial transmission line.