Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer Algorithm
In this paper, an efficient Grey Wolf Optimizer (GWO) search algorithm is presented for solving the optimal power flow problem in a power system, enhanced by wind power plant. The GWO algorithm is based on meta-heuristic method, and it has been proven to give very competitive results in different op...
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| Format: | Article |
| Language: | English |
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VSB-Technical University of Ostrava
2018-01-01
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| Series: | Advances in Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://advances.utc.sk/index.php/AEEE/article/view/2883 |
| _version_ | 1797826976910671872 |
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| author | Sebaa Haddi Omrane Bouketir Tarek Bouktir |
| author_facet | Sebaa Haddi Omrane Bouketir Tarek Bouktir |
| author_sort | Sebaa Haddi |
| collection | DOAJ |
| description | In this paper, an efficient Grey Wolf Optimizer (GWO) search algorithm is presented for solving the optimal power flow problem in a power system, enhanced by wind power plant. The GWO algorithm is based on meta-heuristic method, and it has been proven to give very competitive results in different optimization problems. First, by using the proposed technique, the system independent variables such as the generators’ power outputs as well as the associated dependent variables like the bus voltage magnitudes, transformer tap setting and shunt VAR compensators values are optimized to meet the power system operation requirements. The Optimal power flow study is then performed to assess the impact of variable wind power generation on system parameters. Two standard power systems IEEE30 and IEEE57 are used to test and verify the effectiveness of the proposed GWO method. The obtained results are then compared with others given by available optimization methods in the literature. The outcome of the comparison proved the superiority of the GWO algorithm over other meta-heuristics techniques such as Modified Differential Evolution (MDE), Enhanced Genetic Algorithm (EGA), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Artificial Bee Algorithm (ABC) and Tree-Seed Algorithm (TSA). |
| first_indexed | 2024-04-09T12:40:52Z |
| format | Article |
| id | doaj.art-7e9ba07299f84d10a6c61ca10dc7a4cb |
| institution | Directory Open Access Journal |
| issn | 1336-1376 1804-3119 |
| language | English |
| last_indexed | 2024-04-09T12:40:52Z |
| publishDate | 2018-01-01 |
| publisher | VSB-Technical University of Ostrava |
| record_format | Article |
| series | Advances in Electrical and Electronic Engineering |
| spelling | doaj.art-7e9ba07299f84d10a6c61ca10dc7a4cb2023-05-14T20:50:12ZengVSB-Technical University of OstravaAdvances in Electrical and Electronic Engineering1336-13761804-31192018-01-0116447148810.15598/aeee.v16i4.28831016Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer AlgorithmSebaa Haddi0Omrane Bouketir1Tarek Bouktir2Department of Electrical Engineering, Faculty of technology, University of Setif, El Bez, Sétif 19000, AlgeriaDepartment of Electrical Engineering, Faculty of technology, University of Setif, El Bez, Sétif 19000, AlgeriaDepartment of Electrical Engineering, Faculty of technology, University of Setif, El Bez, Sétif 19000, AlgeriaIn this paper, an efficient Grey Wolf Optimizer (GWO) search algorithm is presented for solving the optimal power flow problem in a power system, enhanced by wind power plant. The GWO algorithm is based on meta-heuristic method, and it has been proven to give very competitive results in different optimization problems. First, by using the proposed technique, the system independent variables such as the generators’ power outputs as well as the associated dependent variables like the bus voltage magnitudes, transformer tap setting and shunt VAR compensators values are optimized to meet the power system operation requirements. The Optimal power flow study is then performed to assess the impact of variable wind power generation on system parameters. Two standard power systems IEEE30 and IEEE57 are used to test and verify the effectiveness of the proposed GWO method. The obtained results are then compared with others given by available optimization methods in the literature. The outcome of the comparison proved the superiority of the GWO algorithm over other meta-heuristics techniques such as Modified Differential Evolution (MDE), Enhanced Genetic Algorithm (EGA), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Artificial Bee Algorithm (ABC) and Tree-Seed Algorithm (TSA).http://advances.utc.sk/index.php/AEEE/article/view/2883grey wolf optimizer (gwo)grey wolvesopf problem. |
| spellingShingle | Sebaa Haddi Omrane Bouketir Tarek Bouktir Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer Algorithm Advances in Electrical and Electronic Engineering grey wolf optimizer (gwo) grey wolves opf problem. |
| title | Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer Algorithm |
| title_full | Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer Algorithm |
| title_fullStr | Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer Algorithm |
| title_full_unstemmed | Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer Algorithm |
| title_short | Improved Optimal Power Flow for a Power System Incorporating Wind Power Generation by Using Grey Wolf Optimizer Algorithm |
| title_sort | improved optimal power flow for a power system incorporating wind power generation by using grey wolf optimizer algorithm |
| topic | grey wolf optimizer (gwo) grey wolves opf problem. |
| url | http://advances.utc.sk/index.php/AEEE/article/view/2883 |
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