Power Quality Disturbances Identification based on the Mode Decomposition and Machine Learning

This paper presents an approach for classification of the power quality disturbances (PQDs). The PQD signals are modelled based on the IEEE 1159–2019 standard. The outcome of the used PQD model is employed for analysing the performance of suggested classification method. Firstly, the PQD signals are segmented and then each segment is decomposed in “intrinsic mode functions” (IMFs) by using the “Empirical Mode Decomposition” (EMD). Afterward, the features are mined from IMFs. The developed features set is further processed by machine learning based classifiers for identification of PQDs. The highest accuracy of 98.90% is secured for the case of a six-classes problem.