Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks

Year 2021, Volume: 4 Issue: 2, 11 - 18, 31.12.2021

Jose Eduardo Urrea Cabus İsmail Hakkı Altaş

Abstract

This paper compares various unsupervised feature extraction techniques and supervised machine learning models for fault detection and classification over a power distributed generation system. The modified IEEE 34 bus test feeder was implemented for the study case simulated through PowerFactory DigSILENT software. Data analysis results from three-phase voltages and currents collected were performed in Python. Simulation results confirm that by applying dimensionality reduction techniques as feature extraction and wavelet family selection adequately, a high identification and classification accuracy can be obtained, excluding the less essential characteristics and preventing the machine learning models from overfitting or underfitting the datasets.

Keywords

Data mining, fault diagnosis, feature extraction, machine learning

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