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Detection and Classification of Single Phase to Ground Faults Under High Resistance Ground Paths in Power Systems using Machine Learning

The detection and classification of power systems anomalies is of paramount importance. The detection of phase to ground faults under high resistance ground paths remains a challenging task for protection engineers since the resulting current remains below the detection threshold of conventional protection relays. This work proposes a combination of discrete wavelet transform (DWT) and k-Nearest Neighbor (KNN) machine learning (ML) algorithm to detect and classify the phase to ground faults under high resistance ground paths. The developed algorithm achieved an accuracy of 100%; other ML classifiers like Naive Bayes (NB) & Support Vector Machine (SVM) were tested under the same conditions achieving an accuracy of 70.4% and 77.2%, respectively. The developed k-NN was further tested under an isolated neutral distribution network and varying fault locations.