PQ event identification in PV-wind based distribution network with variational mode decomposition and novel feature enabled random forest classifier

Abstract

Abstract The complexity raised due to the exponential growth of renewable power generators in modern power system evokes the necessity of detecting the potential power quality threats. With this context, the article proposes a novel power quality (PQ) detection method in PV and wind generator integrated distribution networks. In the first stage, the variational mode decomposition (VMD) technique is utilized to pull out the characteristic modes out of the voltage signal for nine different PQ events. In the later stage, a set of features are extracted from the decomposed signal to train a novel Feature Enabled Random Forest Classifier (FERFC). The feature enabling capability of Random Forest classifier is reckoned with the help of a single decision tree. It has been proved that the proposed novel VMD-FERFC has the ability to accurately identify and classify the PQ disturbances in the PV-wind-based distribution network. The performance of the suggested technique is verified in presence of noise. Further the supremacy of the method is established by comparing with other competitive reported PQ detection methods.