High Impedance Fault Detection Using Multi-Domain Feature with Artificial Neural Network

Abstract

High Impedance Fault (HIF) poses a threat to human life even though the fault current is low in magnitude. Moreover, the proliferation of power electronic devices give rise to a range of harmonics in the system. Hence it is difficult to identify the HIF by employing classical approach. In this paper an algorithm is developed that uses signatures in both time and frequency domain to identify the HIF. Since the parameters are inconsistent at every situation, it is imperative to use a classifying technique such as artificial neural network to distinguish the fault and normal situation. The algorithm is tested in a IEEE 33 bus system with the presence of nonlinear loads. The harmonic loads are simulated as current source and the harmonic content of the loads are taken with the help of Harmonic analyzer. The relevant features are judiciously selected to improve the accuracy of the algorithm to detect the HIF. The multi-domain features selected are the rate of change of phase current, change in the angle of the sequence currents and relative energy of Digital Wavelet Transform (DWT) coefficients. The proposed algorithm is used to distinguish the condition such as load switching, bolted ground fault, non-linear current harmonics from HIF.