  This paper presents a novel approach for fault classification and section
identification in a series compensated transmission line based on least square
support vector machine. The current signal corresponding to one-fourth of the
post fault cycle is used as input to proposed modular LS-SVM classifier. The
proposed scheme uses four binary classifier; three for selection of three
phases and fourth for ground detection. The proposed classification scheme is
found to be accurate and reliable in presence of noise as well. The simulation
results validate the efficacy of proposed scheme for accurate classification of
fault in a series compensated transmission line.
