High impedance fault detection in distribution network using time-frequency based algorithm

A new high impedance fault (HIF) detection method using time-frequency analysis for feature extraction is proposed. A pattern classifier is trained whose feature set consists of current waveform energy and normalized joint time-frequency moments. The proposed method shows high efficacy in all the detection criteria defined in this paper. The method is verified using the real-world data, acquired from HIF tests on three different materials (concrete, grass, and tree branch) and under two different conditions (wet, and dry). Several non-fault events, which often confuse HIF detection systems, were simulated, such as capacitor switching, transformer inrush current, non-linear loads, and power electronics sources. A new set of criteria for fault detection is proposed. Using these criteria the proposed method is evaluated and its performance is compared with the existing methods. These criteria are accuracy, dependability, security, safety, sensibility, cost, objectivity, completeness, and speed. The proposed method is compared with the existing methods, and it is shown to be more reliable, and efficient than its existing counterparts. The effect of choice of pattern classifier on method efficacy is also investigated.