Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system
Generating systems are known as adequately reliable when satisfying the load demand. Meanwhile, the efficiency of electrical systems is currently being more influenced by the growing adoption of the Wind/Solar energy in power systems compared to other conventional power sources. This paper proposed...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Sage Publications
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/86731/ http://psasir.upm.edu.my/id/eprint/86731/1/Metaheuristic%20searching%20genetic%20algorithm%20based%20reliability%20assessment%20of%20hybrid%20power%20generation%20system.pdf |
| _version_ | 1848860274900074496 |
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| author | Abdalla, Ahmed N. Nazir, Muhammad Shahzad Ming, Xin Jiang Kadhem, Athraa Ali Abdul Wahab, Noor Izzri Suqun, Cao Rendong, Ji |
| author_facet | Abdalla, Ahmed N. Nazir, Muhammad Shahzad Ming, Xin Jiang Kadhem, Athraa Ali Abdul Wahab, Noor Izzri Suqun, Cao Rendong, Ji |
| author_sort | Abdalla, Ahmed N. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Generating systems are known as adequately reliable when satisfying the load demand. Meanwhile, the efficiency of electrical systems is currently being more influenced by the growing adoption of the Wind/Solar energy in power systems compared to other conventional power sources. This paper proposed a new optimization approach called Metaheuristic Scanning Genetic Algorithm (MSGA) for the evaluation of the efficiency of power generating systems. The MSGA is based on a combination of metaheuristic scanning and Genetic Algorithm. The MSGA technique is used for evaluating the reliability and adequacy of generation systems integrated with wind/Solar energy is developed. The usefulness of the proposed algorithm was tested using Reliability Test System ‘IEEE-RTS-79’ which include both of wind power and solar power generation. The result approve the effectiveness of the proposed algorithm in improving the computation time by 85% and 2% in comparison with the particle swarm optimization (PSO) and differential evolution optimization algorithm (DEOA) respectively. In addition, the proposed model can be used to test the power capacity forecasting scheme of the hybrid power generation system with the wind, solar and storage. |
| first_indexed | 2025-11-15T12:42:38Z |
| format | Article |
| id | upm-86731 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:42:38Z |
| publishDate | 2020 |
| publisher | Sage Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-867312021-11-10T08:01:25Z http://psasir.upm.edu.my/id/eprint/86731/ Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system Abdalla, Ahmed N. Nazir, Muhammad Shahzad Ming, Xin Jiang Kadhem, Athraa Ali Abdul Wahab, Noor Izzri Suqun, Cao Rendong, Ji Generating systems are known as adequately reliable when satisfying the load demand. Meanwhile, the efficiency of electrical systems is currently being more influenced by the growing adoption of the Wind/Solar energy in power systems compared to other conventional power sources. This paper proposed a new optimization approach called Metaheuristic Scanning Genetic Algorithm (MSGA) for the evaluation of the efficiency of power generating systems. The MSGA is based on a combination of metaheuristic scanning and Genetic Algorithm. The MSGA technique is used for evaluating the reliability and adequacy of generation systems integrated with wind/Solar energy is developed. The usefulness of the proposed algorithm was tested using Reliability Test System ‘IEEE-RTS-79’ which include both of wind power and solar power generation. The result approve the effectiveness of the proposed algorithm in improving the computation time by 85% and 2% in comparison with the particle swarm optimization (PSO) and differential evolution optimization algorithm (DEOA) respectively. In addition, the proposed model can be used to test the power capacity forecasting scheme of the hybrid power generation system with the wind, solar and storage. Sage Publications 2020-09 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/86731/1/Metaheuristic%20searching%20genetic%20algorithm%20based%20reliability%20assessment%20of%20hybrid%20power%20generation%20system.pdf Abdalla, Ahmed N. and Nazir, Muhammad Shahzad and Ming, Xin Jiang and Kadhem, Athraa Ali and Abdul Wahab, Noor Izzri and Suqun, Cao and Rendong, Ji (2020) Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system. Energy Exploration & Exploitation, 39 (1). pp. 488-501. ISSN 0144-5987; ESSN: 2048-4054 https://journals.sagepub.com/toc/eeaa/39/1 10.1177/0144598720959749 |
| spellingShingle | Abdalla, Ahmed N. Nazir, Muhammad Shahzad Ming, Xin Jiang Kadhem, Athraa Ali Abdul Wahab, Noor Izzri Suqun, Cao Rendong, Ji Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system |
| title | Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system |
| title_full | Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system |
| title_fullStr | Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system |
| title_full_unstemmed | Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system |
| title_short | Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system |
| title_sort | metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system |
| url | http://psasir.upm.edu.my/id/eprint/86731/ http://psasir.upm.edu.my/id/eprint/86731/ http://psasir.upm.edu.my/id/eprint/86731/ http://psasir.upm.edu.my/id/eprint/86731/1/Metaheuristic%20searching%20genetic%20algorithm%20based%20reliability%20assessment%20of%20hybrid%20power%20generation%20system.pdf |