An ensemble approach of classification model for detection and classification of power quality disturbances in PV integrated microgrid network

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In this study, different Power Quality Disturbances (PQDs) in Photovoltaic (PV) integrated Microgrid (MG) network have been detected and classified using a voting method of ensemble classification model along with Discrete Wavelet Transform (DWT) analysis. The proposed ensemble classification model is useful to classify the most common PQDs (voltage sag, voltage swell, and harmonics) in islanded MG network, and different PQ transients in both grid-connected and islanded MG network. For this study, a PV integrated MG model has been developed in the Matlab/Simulink software environment with introduction of different PQDs. The result obtained reveals that the performance of proposed ensemble classification model-2 (combination of Bayesian net, Multi-layer perceptron (MLP) and J48 decision tree (JDT) classifiers) attains higher classification accuracy (100%) as compared to other ensemble classification model-1 (combination of Bayes net and MLP classifiers) and base classifiers such as Bayesian net, MLP and JDT. Further, the effectiveness of classifiers has been assessed using performance indices (PI) such as Kappa statistics, Mean absolute error (MAE), Root mean square error (RMSE), Precision, Recall, F-measure, and Receiver operating characteristics (ROC). From the results of PI, it can be concluded that the proposed ensemble model-2 outperforms ensemble model-1 and other base classifiers.