Power Quality Disturbances Classification Based on the Machine Learning Algorithms

mars 2023
Ingénierie & Outils numériques
Communications avec actes dans un congrès international
Auteurs : Omnia Sameer Alghazi (Electrical and Computer Engineering Department), Saeed Mian Qaisar (Electrical and Computer Engineering Department)
Conférence : Research & Innovation Forum, 19 avril 2022

This chapter presents an approach for classification of the power quality disturbances (PQDs). The classification of real-time power quality disturbances (PQDs) is proposed in this work. The PQD signals are modelled based on the IEEE 1159–2019 standard. The outcome of the used PQD model is employed for analyzing the performance of suggested classification method. Firstly, the PQD signals are segmented and then each segment is further processed by machine learning based classifiers for identification of PQDs. The study is conducted for six major classes of the PQDs. The highest identification precision is secured by the Support Vector Machine classifier. It respectively attains the Accuracy = 94.32%, Precision =  84.55%, Recall = 84.33%, Specificity = 96.52%, F-measure = 84.19%, Kappa = 92.59%, and Area Under the Curve (AUC) = 97.83%.