Multi-DGPV Planning Using Artificial Intelligence
This article investigates the impact of multi-Distributed Generation Photovoltaic (DGPV) installation and their degree of penetration on controlling power loss in the radial distribution system. The Integrated Immune Moth Flame Evolution Programming (IIMFEP), a unique hybrid optimization technique,...
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Ismail Saritas
2024
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my.uniten.dspace-342892024-10-14T11:18:51Z Multi-DGPV Planning Using Artificial Intelligence Abdullah A. Musirin I. Othman M.M. Rahim S.R.A. Sentilkumar A.V. 57197864035 8620004100 35944613200 11639107900 56888921600 Backward forward sweep Distributed generation Distributed generation photovoltaic Evolutionary programming Loss minimization Optimization Total voltage deviation This article investigates the impact of multi-Distributed Generation Photovoltaic (DGPV) installation and their degree of penetration on controlling power loss in the radial distribution system. The Integrated Immune Moth Flame Evolution Programming (IIMFEP), a unique hybrid optimization technique, was utilized to identify the ideal DGPV size and location for base case conditions and under load variations. The IIMFEP approach is compared against Evolutionary Programming (EP), Artificial Immune System (AIS), and Moth Flame Optimization (MFO) and validated using the IEEE 118-Bus Radial Distribution Systems (RDS). Incorporating multi-DGPV into a system reduces the total real and reactive power loss while simultaneously increasing the minimum voltage and decreasing the total voltage deviation. In every instance examined in this study, the IIMFEP method yields optimal solutions superior to those generated by the other three methods. As the number of DGPV units increased to nine, the percentage of power loss reduction became the highest among all DG units examined, and DG penetration reached 94.26 percent. This research provides the power system operator with comprehensive findings demonstrating the impact of installing multi-DGPV in distribution networks on system loss. � 2023, Ismail Saritas. All rights reserved. Final 2024-10-14T03:18:51Z 2024-10-14T03:18:51Z 2023 Article 2-s2.0-85161668041 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161668041&partnerID=40&md5=b9dd6777ee6339f6e73e67cb03e0b0d0 https://irepository.uniten.edu.my/handle/123456789/34289 11 4s 377 391 Ismail Saritas Scopus |
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Backward forward sweep Distributed generation Distributed generation photovoltaic Evolutionary programming Loss minimization Optimization Total voltage deviation |
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Backward forward sweep Distributed generation Distributed generation photovoltaic Evolutionary programming Loss minimization Optimization Total voltage deviation Abdullah A. Musirin I. Othman M.M. Rahim S.R.A. Sentilkumar A.V. Multi-DGPV Planning Using Artificial Intelligence |
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This article investigates the impact of multi-Distributed Generation Photovoltaic (DGPV) installation and their degree of penetration on controlling power loss in the radial distribution system. The Integrated Immune Moth Flame Evolution Programming (IIMFEP), a unique hybrid optimization technique, was utilized to identify the ideal DGPV size and location for base case conditions and under load variations. The IIMFEP approach is compared against Evolutionary Programming (EP), Artificial Immune System (AIS), and Moth Flame Optimization (MFO) and validated using the IEEE 118-Bus Radial Distribution Systems (RDS). Incorporating multi-DGPV into a system reduces the total real and reactive power loss while simultaneously increasing the minimum voltage and decreasing the total voltage deviation. In every instance examined in this study, the IIMFEP method yields optimal solutions superior to those generated by the other three methods. As the number of DGPV units increased to nine, the percentage of power loss reduction became the highest among all DG units examined, and DG penetration reached 94.26 percent. This research provides the power system operator with comprehensive findings demonstrating the impact of installing multi-DGPV in distribution networks on system loss. � 2023, Ismail Saritas. All rights reserved. |
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57197864035 |
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57197864035 Abdullah A. Musirin I. Othman M.M. Rahim S.R.A. Sentilkumar A.V. |
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Article |
author |
Abdullah A. Musirin I. Othman M.M. Rahim S.R.A. Sentilkumar A.V. |
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Abdullah A. |
title |
Multi-DGPV Planning Using Artificial Intelligence |
title_short |
Multi-DGPV Planning Using Artificial Intelligence |
title_full |
Multi-DGPV Planning Using Artificial Intelligence |
title_fullStr |
Multi-DGPV Planning Using Artificial Intelligence |
title_full_unstemmed |
Multi-DGPV Planning Using Artificial Intelligence |
title_sort |
multi-dgpv planning using artificial intelligence |
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Ismail Saritas |
publishDate |
2024 |
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1814061114482229248 |