Capacity‐based optimization using whale optimization technique of a power distribution network
Abstract In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.12455 |
| _version_ | 1798021248686489600 |
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| author | Bawoke Simachew Baseem Khan Josep M. Guerrero Sanjeevikumar Padmanaban Om Prakash Mahela Hassan Haes Alhelou |
| author_facet | Bawoke Simachew Baseem Khan Josep M. Guerrero Sanjeevikumar Padmanaban Om Prakash Mahela Hassan Haes Alhelou |
| author_sort | Bawoke Simachew |
| collection | DOAJ |
| description | Abstract In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi‐objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi‐objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method. |
| first_indexed | 2024-04-11T17:10:40Z |
| format | Article |
| id | doaj.art-b131debd250c42c581cdd768a05200fc |
| institution | Directory Open Access Journal |
| issn | 2577-8196 |
| language | English |
| last_indexed | 2024-04-11T17:10:40Z |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj.art-b131debd250c42c581cdd768a05200fc2022-12-22T04:12:55ZengWileyEngineering Reports2577-81962022-01-0141n/an/a10.1002/eng2.12455Capacity‐based optimization using whale optimization technique of a power distribution networkBawoke Simachew0Baseem Khan1Josep M. Guerrero2Sanjeevikumar Padmanaban3Om Prakash Mahela4Hassan Haes Alhelou5Department of Electrical Engineering National Taipei University of Technology Taipei TaiwanDepartment of Electrical and Computer Engineering Hawassa University Awasa EthiopiaCenter for Research on Microgrids – CROM Aalborg University Aalborg DenmarkCTiF Global Capsule, Department of Business Development and Technology Aarhus University Herning DenmarkPower System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd. Jaipur IndiaDepartment of Electrical Power Engineering Tishreen University Lattakia SyriaAbstract In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. Distributed generation (DG) and capacitor bank (QG) can be utilized to solve these issues. Therefore, this paper utilized optimally placed and sized DG and capacitor (QG) to minimize losses and improve the voltage profile. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called multi‐objective whale optimization algorithm (MWOA). Wind and solar photovoltaic sources with biomass are utilized as the DG sources with their probabilistic outputs. The developed method is tested using two practical feeders of Bahir Dar city distribution network, Ethiopia. The results of loss minimization and voltage profile enhancement with MWOA are compared with multi‐objective particle swam optimization (MPSO) with an equal number of iterations to show the superiority of the developed method.https://doi.org/10.1002/eng2.12455capacitor placementdistributed generationloss minimizationparticle swarm optimizationwhale optimization algorithm |
| spellingShingle | Bawoke Simachew Baseem Khan Josep M. Guerrero Sanjeevikumar Padmanaban Om Prakash Mahela Hassan Haes Alhelou Capacity‐based optimization using whale optimization technique of a power distribution network Engineering Reports capacitor placement distributed generation loss minimization particle swarm optimization whale optimization algorithm |
| title | Capacity‐based optimization using whale optimization technique of a power distribution network |
| title_full | Capacity‐based optimization using whale optimization technique of a power distribution network |
| title_fullStr | Capacity‐based optimization using whale optimization technique of a power distribution network |
| title_full_unstemmed | Capacity‐based optimization using whale optimization technique of a power distribution network |
| title_short | Capacity‐based optimization using whale optimization technique of a power distribution network |
| title_sort | capacity based optimization using whale optimization technique of a power distribution network |
| topic | capacitor placement distributed generation loss minimization particle swarm optimization whale optimization algorithm |
| url | https://doi.org/10.1002/eng2.12455 |
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