Fault Location Identifications in HV Transmission Networks and Different MV Wind Farms Using Nonintrusive Monitoring Techniques

Abstract

The transmission-line fault localization of high-voltage (HV) transmission networks and different medium-voltage wind farms is implemented using a global time–frequency analysis algorithm, energy-spectrum-based hyperbolic S-transform (HS), to extract the potential power signatures from monitoring nonlinear and nonstationary fault signals on HV power utility. An energy concentration algorithm transforms each HS coefficient into useful features to quantify various faulty events and reduce recognition algorithms’ inputs. Furthermore, the multiclass classifier processes fault location identification. The simulation results show that the proposed method achieves a high classification rate for considering fault inception angles and fault resistance uncertainty.