A Simple Approach to Detect High Impedance Fault Using Morphological Gradient Edge Detector

Detection of high impedance faults (HIF) is one of the biggest challenges in power distribution networks. HIF usually occurs when conductors in the distribution network are broken and accidently come into contact the ground or a tree branch. The current of this fault is close to the load current level and cannot be detected by overcurrent relays. Also, some regular system phenomena such as capacitor switching, load switching, and inrush current and saturation phenomena in current transformer (CT) represent some features which may overlap the components of HIF; making HIF detection schemes more complex. In this paper, a new method for HIF detection is presented which is able to distinguish any type of HIF from regular system phenomena. To achieve this, the scheme of morphological gradient edge detection (MGED) is used to process voltage signals. The MGED extracts two main features from the processed signals: first, the edges or changes in the signal are elicited and then, these features are extracted after two cycles from the onset of the fault. Then, based on these features, a high impedance fault detection index (HIFDI) is introduced for distinguishing and classifying HIF from other regular system phenomena. The simulation results for different types of HIF fault in a sample 20 kV distribution feeder and IEEE 34-bus distribution test system using EMTP confirm the fast and accurate performance of the proposed method.