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Power system events classification using genetic algorithm based feature weighting technique for support vector machine

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dc.contributor.author Alimi, OA
dc.contributor.author Ouahada, K
dc.contributor.author Abu-Mahfouz, Adnan M
dc.contributor.author Rimer, S
dc.date.accessioned 2021-07-19T07:31:08Z
dc.date.available 2021-07-19T07:31:08Z
dc.date.issued 2021-01
dc.identifier.citation Alimi, O., Ouahada, K., Abu-Mahfouz, A.M. & Rimer, S. 2021. Power system events classification using genetic algorithm based feature weighting technique for support vector machine. <i>Heliyon, 7(1).</i> http://hdl.handle.net/10204/12060 en_ZA
dc.identifier.issn 2405-8440
dc.identifier.uri https://doi.org/10.1016/j.heliyon.2021.e05936
dc.identifier.uri http://hdl.handle.net/10204/12060
dc.description.abstract Currently, ensuring that power systems operate efficiently in stable and secure conditions has become a key challenge worldwide. Various unwanted events including injections and faults, especially within the generation and transmission domains are major causes of these instability menaces. The earlier operators can identify and accurately diagnose these unwanted events, the faster they can react and execute timely corrective measures to prevent large-scale blackouts and avoidable loss to lives and equipment. This paper presents a hybrid classification technique using support vector machine (SVM) with the evolutionary genetic algorithm (GA) model to detect and classify power system unwanted events in an accurate yet straightforward manner. In the proposed classification approach, the features of two large dimensional synchrophasor datasets are initially reduced using principal component analysis before they are weighted in their relevance and the dominant weights are heuristically identified using the genetic algorithm to boost classification results. Consequently, the weighted and dominant selected features by the GA are utilized to train the modelled linear SVM and radial basis function kernel SVM in classifying unwanted events. The performance of the proposed GA-SVM model was evaluated and compared with other models using key classification metrics. The high classification results from the proposed model validates the proposed method. The experimental results indicate that the proposed model can achieve an overall improvement in the classification rate of unwanted events in power systems and it showed that the application of the GA as the feature weighting tool offers significant improvement on classification performances. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S2405844021000414 en_US
dc.source Heliyon, 7(1) en_US
dc.subject Genetic algorithm en_US
dc.subject Power system en_US
dc.subject Support vector machine en_US
dc.subject Synchrophasors en_US
dc.title Power system events classification using genetic algorithm based feature weighting technique for support vector machine en_US
dc.type Article en_US
dc.description.pages 9 en_US
dc.description.note © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/4.0/). en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea EDTRC Management en_US
dc.identifier.apacitation Alimi, O., Ouahada, K., Abu-Mahfouz, A. M., & Rimer, S. (2021). Power system events classification using genetic algorithm based feature weighting technique for support vector machine. <i>Heliyon, 7(1)</i>, http://hdl.handle.net/10204/12060 en_ZA
dc.identifier.chicagocitation Alimi, OA, K Ouahada, Adnan M Abu-Mahfouz, and S Rimer "Power system events classification using genetic algorithm based feature weighting technique for support vector machine." <i>Heliyon, 7(1)</i> (2021) http://hdl.handle.net/10204/12060 en_ZA
dc.identifier.vancouvercitation Alimi O, Ouahada K, Abu-Mahfouz AM, Rimer S. Power system events classification using genetic algorithm based feature weighting technique for support vector machine. Heliyon, 7(1). 2021; http://hdl.handle.net/10204/12060. en_ZA
dc.identifier.ris TY - Article AU - Alimi, OA AU - Ouahada, K AU - Abu-Mahfouz, Adnan M AU - Rimer, S AB - Currently, ensuring that power systems operate efficiently in stable and secure conditions has become a key challenge worldwide. Various unwanted events including injections and faults, especially within the generation and transmission domains are major causes of these instability menaces. The earlier operators can identify and accurately diagnose these unwanted events, the faster they can react and execute timely corrective measures to prevent large-scale blackouts and avoidable loss to lives and equipment. This paper presents a hybrid classification technique using support vector machine (SVM) with the evolutionary genetic algorithm (GA) model to detect and classify power system unwanted events in an accurate yet straightforward manner. In the proposed classification approach, the features of two large dimensional synchrophasor datasets are initially reduced using principal component analysis before they are weighted in their relevance and the dominant weights are heuristically identified using the genetic algorithm to boost classification results. Consequently, the weighted and dominant selected features by the GA are utilized to train the modelled linear SVM and radial basis function kernel SVM in classifying unwanted events. The performance of the proposed GA-SVM model was evaluated and compared with other models using key classification metrics. The high classification results from the proposed model validates the proposed method. The experimental results indicate that the proposed model can achieve an overall improvement in the classification rate of unwanted events in power systems and it showed that the application of the GA as the feature weighting tool offers significant improvement on classification performances. DA - 2021-01 DB - ResearchSpace DP - CSIR J1 - Heliyon, 7(1) KW - Genetic algorithm KW - Power system KW - Support vector machine KW - Synchrophasors LK - https://researchspace.csir.co.za PY - 2021 SM - 2405-8440 T1 - Power system events classification using genetic algorithm based feature weighting technique for support vector machine TI - Power system events classification using genetic algorithm based feature weighting technique for support vector machine UR - http://hdl.handle.net/10204/12060 ER - en_ZA
dc.identifier.worklist 24801 en_US


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