High Impedance Fault Detection Based on Wavelet Transform and Statistical Pattern Recognition

Abstract

A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.

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

“…[30] Redial 97.3 % NA Ref. [5] Redial 96.0 % NA Ref. [12] Redial 100 % NA used as learning algorithm.…”

“…Table 9 demonstrates the comparison between the proposed stransform based technique, and existing/proposed HIF detection methods. The information related to SNR 30 dB case are not available in the reference paper [5,12,29,30] and is shown as NA (not available) in Table 9. The table clearly reveals an inherent performance of the technique to detect high impedance fault under noisy condition…”

“…Identification techniques generally contain two vital steps: feature extraction and classification [2]. For many years, some protection engineers and researchers had introduced numerous feature extraction and classification techniques such as digital signal processing [3], fractal analysis [4], wavelet transform (WT) [5][6][7][8][9][10], crest factor [11], coefficient of variation [11], mathematical morphology [12], Kalman filtering [13], decision tree [14] etc. and various classifiers have been used such as Bays, nearest neighbor rule (NNR), artificial neural network (ANN) [15], ANFIS (adaptive neuro-fuzzy inference system) [16] etc.…”

Expansion of the Decoupled Discreet-Time Jacobian Eigenvalue Approximation for Model-Free Analysis of PMU Data

“…Wavelet transforms are applied in [12] [14] [15] [16] [17] to detect HIF cases and distinguish the multilevel characteristics in order to identify events. However, these applications used transient phase current data.…”

“…If an X value is substituted, the weights will return the overall change in bus power. The final linear model for bus 1 is shown in Equation (15) and expanded generically in Equation (16). R code was used to carry out this analysis.…”