Evaluation of the Effects of Noise and Sampling Rate on Detection of High Impedance Fault with Machine Learning Methods on the Distribution System

High impedance faults (HIF) are complex faults that affect the stability and reliability of the distribution networks. In the last decades, machine learning (ML) methods have been used extensively to detect such faults. However, their performances were not evaluated completely in the studies. Thus, an analysis of different ML methods, which are artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbors (KNN) was presented in this paper for HIF identification. The precision of methods was tested and compared for high impedance fault detection by considering the effects of noise and sampling rate. Moreover, a combination of the discrete wavelet transform (DWT) with machine learning methods was investigated. A comparison was made with the accuracy rate of algorithms in distinguishing HIFs from other events. IEEE 34 busses test network was modeled in Matlab/Simulink, and current waveforms are utilized for training the algorithms. Based on the results, it is revealed that the accuracy of all algorithms was decreased with lower sampling rates and higher noise ratios in the signal. In terms of the comparison of methods, KNN became the most endurance algorithm to change in sampling rate, while ANN was the least influenced by noise. Additionally, SVM showed better precisions when noise is not added to the signal.