A novel nonintrusive fault identification for power transmission networks using power-spectrum-based hyperbolic S-transform — Part I: Fault classification

Abstract

This paper presents a novel nonintrusive protection scheme for fault classification of power transmission networks in a wide-area measurement system (WAMS) using fault information for decision making. The protection scheme is a non-communication without GPS as it depends completely on locally measured currents for the non-intrusive fault monitoring (NIFM) using the power-spectrum-based hyperbolic S-transform (PS-HST). In this work, the HST is used to extract the high frequency components of the current signals generated by an electric fault. To effectively select the HST coefficients (HSTCs) representing fault transient signals with increasing performance, a power spectrum of the HSTCs in different scales calculated by Parseval's Theorem is proposed in this paper. Finally, back-propagation artificial neural networks (BP-ANNs) and PS-HST are used to identify fault types in power transmission networks. The proposed method is tested for different breaker ON/ OFF conditions by simulations using electromagnetic transients program (EMTP) software. The results obtained have proved that the proposed method is promising and demonstrate a high success rates and reliability for considering different fault resistances and inception angles in NIFM applications.