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Protection from the high impedance fault (HIF) has been one of the biggest challenges in the power distribution network. HIF typically occurs when the conductors in distribution network break and touch the ground surface; or lean and touch a tree branch. This fault, with current magnitude close to the load current level, is not detectable by over-current relays. This paper aims to review the literature related to the HIF phenomenon. In this work, the HIF detection techniques are categorized, evaluated, and compared with one another. Furthermore, the existing HIF models and HIF location techniques are reviewed. Finally, the shortcoming of the existing perspective toward the HIF phenomenon and the possible road to the future for HIF detection is discussed. [ABSTRACT FROM AUTHOR]

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 of the detection criteria defined in this paper. The method is verified using 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 nonfault events, which often confuse HIF detection systems, were simulated, such as capacitor switching, transformer inrush current, nonlinear 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 the pattern classifier on method efficacy is also investigated. [ABSTRACT FROM AUTHOR]