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Title: A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network
Authors: Vinayagam, Arangarajan
Mohammad Lutfi Othman
Veerasamy, Veerapandiyan
Balaji, Suganthi Saravan
Ramaiyan, Kalaivani
Radhakrishnan, Padmavathi
Raman, Mohan Das
Noor Izzri Abdul Wahab
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Vinayagam, A., Mohammad Lutfi Othman, Veerasamy, V., Balaji, S. S., Ramaiyan, K., Radhakrishnan, P., Raman, M. D. & Noor Izzri Abdul Wahab (2022). A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network. PloS One, 17(1), e0262570-.
Journal: PloS one
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.
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0262570
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 Vinayagam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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