Signal-Energy based Fault Classification of Single- Circuit Transmission Line using S-Transform and Neural Network

This paper presents a technique for diagnosis of the type of fault and the faulty phase on overhead transmission line. The proposed method is based on the multiresolution S-transform and Parseval’s theorem. S-transform is used to produce instantaneous frequency vectors of the voltage signals of the three phases, and then the energies of these vectors, based on the Parseval’s theorem, are utilized as inputs to a Probabilistic Neural Network (PNN). The power system network considered in this study is three phase Transmission line with balanced loading simulated in the PowerSim Toolbox of MATLAB. The fault conditions are simulated by the variation of fault location, fault resistance, fault inception angle. The training is conducted by programming in MATLAB. The robustness of the proposed scheme is investigated by synthetically polluting the simulated voltage signals with White Gaussian Noise. The suggested method has produced fast and accurate results. Estimation of fault location is intended to be conducted in future.