Combined Mathematical Morphology and Data Mining Based High Impedance Fault Detection

This paper presents an intelligent scheme for high impedance fault detection using mathematical morphology and decision tree. The current signals are pre-processed using mathematical morphology and estimation of the signal features is used to generate a decision tree model. The final relaying operation based on generated data mining decision tree model. The proposed method is tested on a standard test system with a wide range of power system operating conditions. Simulation results show that the proposed method can be highly reliable in detecting high impedance fault for harmless and secured operations. (C) 2017 The Authors. Published by Elsevier Ltd.