Classifying Power Quality Disturbances in Noisy Conditions using Machine Learning

Abstract

When ensuring high-quality power supply of the power grid it is of the upmost importance to correctly detect and classify any power quality (PQ) disturbance. Selecting the most relevant features is very important in the process of training a genera machine learning model. Therefore, we analyze the power signals and extract information from them, and then select the most significant features. Additionally, an effective classification model is required. In this study we apply grid search throughout the features sets on one side, and the classification algorithms on the side. This way, we determine the most effective combination of an algorithm and feature set for classification of power quality disturbances.