Detection and Classification of Power Quality Disturbances using Machine Learning Algorithms

Power quality disturbances (PQDs) have affected many people due to the growing number of electronic nonlinear loads and because of the significant increase of renewable sources connected to the grid. Previous works have shown the development of algorithms to detect and classify these disturbances. A thorough review of PQD detector algorithms pointed out the use of machine learning and deep learning algorithms as the most used, accurate and up-to-date approaches to deal with this problem. This work presents a study of Machine Learning algorithms for the detection and classification of power quality disturbances.