Fault Classification for Transmission Lines Based on Group Sparse Representation

Abstract

Fault classification is an important aspect of the protective relaying system for transmission lines. This paper proposes a new method based on group sparse representation for fault classification in transmission lines in which half-cycle superimposed current signals are measured for the classification task. When compared to conventional feature extraction methods, the proposed method in this paper alleviates the requirement to manually design feature. Signals are factorized over an over-complete basis in which elements are the fault signals themselves. The algorithm of classification is based on the idea that the training samples of a particular fault type approximately form a linear basis for any test sample belonging to that class. Solved by ${l}^{{2,1}}$ -minimization, the coefficient should be group sparse, and its non-zero entries correspond to particular group of correlated training samples. It is illustrated that the proposed classification method can be properly modified to deal with noise-containing signals. Moreover, dimension reduction is performed using random mapping technique. The results of several simulations are carried out by PSCAD/EMTDC and field data in real system indicate that the proposed method is accurate and fast for fault classification, and has a high robustness to noise.