A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network.

Arangarajan Vinayagam,Veerapandiyan Veerasamy,Padmavathi Radhakrishnan,Mohan Das Raman,Kalaivani Ramaiyan,Mohammad Lutfi Othman,Noor Izzri Abdul Wahab,Suganthi Saravan Balaji,Sathishkumar V E

doi:10.1371/journal.pone.0262570

Arangarajan Vinayagam, Veerapandiyan Veerasamy + Show 7 more

Open Access

https://doi.org/10.1371/journal.pone.0262570

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Abstract

This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.

Highlights

Microgrid (MG) generally provides reliable, economic, and secured energy supply to the critical loads and remote areas of communities, with following additional features: promotes demand side management; low carbon emission of energy supply; accommodates multiple generating options from different types of Distributed Generation (DG) sources, and so on [1]
The results of PQ analysis that includes extraction of features with the Discrete Wavelet Transform (DWT) method and classification of various Power Quality Events (PQEs) in the MG network are discussed in detail
In the pre-stage of classification, the features of energy values from the disturbance signals of various PQEs are extracted by the DWT technique

Summary

Introduction

Microgrid (MG) generally provides reliable, economic, and secured energy supply to the critical loads and remote areas of communities, with following additional features: promotes demand side management; low carbon emission of energy supply; accommodates multiple generating options from different types of Distributed Generation (DG) sources, and so on [1]. It is a major challenge to maintain the quality of energy supply in the MG network while. A random subspace ensemble classifier for discrimination of power quality events in solar PV microgrid network

Methods

Results

Conclusion