Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model

Abstract

An irregular activity on electric power distribution feeder, which does not draw adequate fault current to be detected by general protective devices, is called as High impedance fault (HIF). This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. This paper proposes an intelligent algorithm using the Takagi Sugeno- Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. The Fast Fourier Transformation (FFT) is used to extract the feature of the faulted signals and other power system events. The effect of capacitor bank switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. The HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. It is evident from the outcomes that the proposed algorithm can effectively differentiate the HIFs from other events in power distribution feeder