High impedance fault detection in microgrid using maximal overlapping discrete wavelet transform and decision tree

Abstract

The paper presents a high impedance fault (HIF) detection scheme for microgrid using maximal overlapping discrete wavelet transform (MODWT) and decision tree (DT). The small, nonlinear, random and widely varying fault current of HIF makes the over-current relay insensitive. Thus, time-frequency information is required to distinguish HIF from no-fault event. The proposed work start with retrieving the fault current which is pre-process through (decompose using) MODWT to get the details and approximation coefficient. Further, some statistical features are estimated using the details and approximation coefficient. The features extracted are used to train decision tree (DT) for accurate classification of HIF from no-fault. The proposed scheme is tested for HIFs detection in microgrid for different operating conditions. The test results show that the proposed scheme provides an effective protection measure against HIFs for safe and secured operation of microgrid.