Discrimination of high impedance fault in microgrid power network using semi-supervised machine learning algorithm

Abstract

This work proposes a semi-supervised classification approach for discriminating high-impedance (HI) faults and other transients in a photovoltaic (PV) interconnected microgrid (MG) network. The suggested classifier combines unsupervised K-means clustering with the supervised multi-layer perceptron neural network algorithm. The K-means clustering technique is utilized in the first phase to detect and remove irrelevant instances from multiple events in the data set. To obtain the final predictions of targeted labels, clustered cases from the first phase are utilized to learn the multi-layer perceptron neural network classifier in the next phase. The suggested method outperforms stand-alone classifiers (K-means clustering and multi-layer perceptron) by providing enhanced accuracy and success rate of discriminating HI fault under standard test conditions and weather intermittency of PV. Furthermore, the results of the performance study clearly show that the suggested model is more resilient and offers superior performance than the stand-alone classifiers under the standard test condition and uncertainty of PV in MG networks.