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 using fault information for decision making. The protection scheme is a noncommunication without global positioning system as it depends completely on locally measured currents for the nonintrusive 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 and PS-HST are used to identify fault classes in power transmission networks. The proposed method is tested for different breaker on/off conditions by simulations using electromagnetic transients program 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 times in NIFM applications.