A Universal High Impedance Fault Detection Technique for Distribution System Using S-Transform and Pattern Recognition

Abstract

This paper describes a new method for high impedance fault (HIF) detection based on s-transform (ST) and pattern recognition technique. Conventional distance, over current and ground fault relays are difficult to apply for High Impedance Fault (HIF) detection in distribution line because of sensitivity, diversity, selectivity issues in case of low value of fault current. Recently, s-transform has been successfully applied for different power system protection problem. It is a very useful tool to analyze transient fault signal to provide both time and frequency information unlike Fourier transform and the same has been considered for high impedance fault detection in this work. The features extracted using s-transform to train and test the two different intelligent classifier like artificial neural network (ANN) and support vector machine (SVM) separately, to discriminate the HIF with other transient phenomenon (Load switching, capacitor Switching) and also normal fault condition. A comparative study of these two classifiers has been reported based on their detection accuracy. It has been found that the proposed techniques are highly effective for high impedance fault detection under a wide range of operating conditions and noisy environment in a high voltage distribution

AI in arcing‐HIF detection: a brief review

“…Benefited from rich high‐ and low‐frequency information, instability and non‐linear characteristics, the current signal can be used to detect arcing‐HIF. Mishra et al [15] draw energy–time plot and magnitude–time plot of current to extract fault features. Li et al [16] detect arcing‐HIF through the correlation analysis of phase voltage and residual current.…”

“…Time‐domain analysis can extract time irregularity of arcing‐HIF waveform. Dissipation factor of active power is used to identify load switching operation and arcing‐HIF in [15]. Das [17] embraces the four morphological operations to detect HIF.…”

Detection and classification of micro‐grid faults based on HHT and machine learning techniques

“…In the case study, a total number of 1320 low impedance faults (LIF), 1050 HIF and 400 no-fault cases are simulated considering various operational circumstances as stated in Table 3. The HIF model is simulated by non-linear resistance, anti-parallel diode and DC sources as mentioned in [18]. The HIF fault model and the non-linearity of the voltage-current curve are shown in Fig.…”

Method of Single Phase Short Circuit Detection on Overhead Transmission Lines of an Industrial Plant Electric Power Supply System

“…Оптимизации размещения устройств ОМП в электрической сети, действующих на основе генетического алгоритма Чу-Бэсли, посвящена статья [15]. Алгоритм идентификации вида повреждений на основе нечеткой логики с использованием дискретного вейвлет-преобразования разработан авторами [16]. В статье [17] разра-ботан метод обнаружения высокоимпедансных повреждений на основе s-преобразования.…”