| Summary: | This paper presents a Morphology Gradient (MG) filter-based method for detecting High Impedance Faults (HIFs) from non-HIF events. A MG filter is used to extract the statistical features of the current signal. Then, a Random Forest (RF) rule set is created from the statistical features. These rules are with crisp boundary values. Hence, fuzzy membership functions and a fuzzy rule base is developed to discriminate HIF from non-HIF events. From the results of the proposed method, it is found that the method could detect and differentiate HIFs from normal system events with high dependability. Characteristics of the proposed method are analyzed through a broad simulation study of an actual distribution system and found that this method is capable of detecting HIF with negligible effect of variation in operating conditions. Keywords: High Impedance Fault, Morphology Gradient, Random Forest, Fuzzy rule base, Non-linear load
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