High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

Abstract

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.

Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier

“…This is achieved through an advanced fault classification technique that supports an effective, reliable, fast and secured way of relaying operation in the protective system [4]. A numerous study were made for the location of fault in the transmission lines as presented in the literature, only a few of these studies consider the effect of a FACTS-compensated line, and others fail to consider their effects [5][6][7][8][9][10]. The problem of over-reach and under-reach conditions due to the injection and absorption of reactive power by STATCOM into the system leads to a false tripping of the relay [11].…”

“…Numerous computational intelligence classifiers were proposed in the literature for the location of faults in the system, such as the multilayer perceptron (MLP) neural network, support vector machine (SVM), fuzzy logic, particle swarm optimization (PSO), and so on. The Artificial Neural Network (ANN) and SVM classifiers consume large time for training, and also the efficacy of fuzzy depends upon rules framed by the expertise [6,7,13,23,24]. Besides, many different methods of classifier are proposed in the literature, ranging from a heuristic rule of thumb to formal mathematics [24].…”

Detection of high‐impedance fault in distribution network based on time–frequency entropy of wavelet transform

“…It is well known that the wavelet analysis has good performance on time frequency analysis . For instance, reference proposed a protection algorithm developed observes the phase displacement between wavelet coefficients calculated for zero‐sequence voltage and current signals at a chosen high‐level frequency. Due to the effectiveness of wavelet transform on transient analysis, this technique has been combined with other techniques with the aim of improving the reliability of detect schemes.…”

Research on a Faulty Line Selection Method Based on the Zero-Sequence Disturbance Power of Resonant Grounded Distribution Networks

“…The FLS methods based on transient information are expected to further improve the accuracy of FLS in distribution networks, and have been a focus for relevant researchers in recent years. FLS methods based on transient information have been proposed, such as high-frequency component [13], wavelet transform [14,15], neural network [16], expert system [17], extreme learning machine [18], zero-sequence reactive power method [19], transient energy method [20,21] etc. Moreover [22] and [23] discussed synthesis algorithms for FLS.…”