Machine Learning-Based Power Quality Disturbance Classification Under Varying Fundamental Frequency Conditions for Islanding of a Microgrid

The integration of renewable energy sources into modern power systems is increasing rapidly. With their integration into the grid and power electronics devices, the rise in Power Quality Disturbances (PQDs) is posing as a major issue in improving the performance of smart grids and microgrid systems. Due to the intermittency of renewable energy sources, the fundamental frequency of power systems fluctuates, stemming different PQDs. In this work, we present a comparative analysis of four different machine learning algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Naive Bayes (NB), in classifying different types of single-stage and multiple-stage PQDs. We generated a dataset consisting of 29 types of PQDs, while taking into consideration the variation of fundamental frequency. We note that variation of fundamental frequency can significantly affect the classification accuracy of the machine learning models. We introduced different levels of noise to the PQD data to mimic real-life scenarios. We used the Linear Discriminant Analysis (LDA) for dimensionality reduction and feature extraction. Our results show that the classification accuracy decreases as the noise level is increased, and that a decrease in the classification accuracy is possible when the variation of the fundamental frequency is considered.