Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration
Dynamic models; Photovoltaic cells; Structural optimization; Current difference; Dynamic reconfiguration process; Multi objective; Multiple local maximums; Optimal structures; Photovoltaic arrays; Shadow phenomena; Switching matrix; Solar power generation
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2023
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my.uniten.dspace-256402023-05-29T16:12:08Z Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration Yousri D. Thanikanti S.B. Balasubramanian K. Osama A. Fathy A. 56688582500 56267551500 56654475000 57194111401 25654839300 Dynamic models; Photovoltaic cells; Structural optimization; Current difference; Dynamic reconfiguration process; Multi objective; Multiple local maximums; Optimal structures; Photovoltaic arrays; Shadow phenomena; Switching matrix; Solar power generation One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. Multiple local maximum power point (MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This article proposes a recent approach based on Multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across 9-9 PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels. � 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. Final 2023-05-29T08:12:07Z 2023-05-29T08:12:07Z 2020 Article 10.1109/ACCESS.2020.3018722 2-s2.0-85097356820 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097356820&doi=10.1109%2fACCESS.2020.3018722&partnerID=40&md5=94596a944802457aff5cafe8d7fa170c https://irepository.uniten.edu.my/handle/123456789/25640 8 159931 159946 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus |
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Dynamic models; Photovoltaic cells; Structural optimization; Current difference; Dynamic reconfiguration process; Multi objective; Multiple local maximums; Optimal structures; Photovoltaic arrays; Shadow phenomena; Switching matrix; Solar power generation |
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56688582500 |
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56688582500 Yousri D. Thanikanti S.B. Balasubramanian K. Osama A. Fathy A. |
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Yousri D. Thanikanti S.B. Balasubramanian K. Osama A. Fathy A. |
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Yousri D. Thanikanti S.B. Balasubramanian K. Osama A. Fathy A. Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration |
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Yousri D. |
title |
Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration |
title_short |
Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration |
title_full |
Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration |
title_fullStr |
Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration |
title_full_unstemmed |
Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration |
title_sort |
multi-objective grey wolf optimizer for optimal design of switching matrix for shaded pv array dynamic reconfiguration |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2023 |
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1806427371946901504 |
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13.222552 |