Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators
This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators� maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling p...
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Natural Sciences Publishing
2024
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| author | Ismail F.B. Randhawa G.S. Al-Bazi A. Alkahtani A.A. |
| author2 | 58027086700 |
| author_facet | 58027086700 Ismail F.B. Randhawa G.S. Al-Bazi A. Alkahtani A.A. |
| author_sort | Ismail F.B. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators� maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling prolongs the shelf life of the generators and prevents unexpected failures. To reduce the cost and duration of generator maintenance, these models are built with various constants, fitness functions, and objective functions. The Analytical Hierarchy Process (AHP), a decision-making tool, is implemented to aid the researcher in prioritizing and re-ranking the maintenance activities from the most important to the least. The intelligent optimization models are developed using MATLAB and the developed intelligent algorithms are tested on a case study in a coal power plant located at minjung, Perak, Malaysia. The power plant is owned and operated by Tenaga Nasional Berhad (TNB), the electric utility company in peninsular Malaysia. The results show that GA outperforms ACO since it reduces maintenance costs by 39.78% and maintenance duration by 60%. The study demonstrates that the proposed optimization method is effective in reducing maintenance time and cost while also optimizing power plant operation. � 2023 NSP Natural Sciences Publishing Cor. |
| format | Article |
| id | my.uniten.dspace-34690 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Natural Sciences Publishing |
| record_format | dspace |
| spelling | my.uniten.dspace-346902024-10-14T11:21:46Z Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators Ismail F.B. Randhawa G.S. Al-Bazi A. Alkahtani A.A. 58027086700 58080315400 35098298500 55646765500 Ant-Colony Optimization Generator Genetic Algorithm Maintenance Scheduling Optimization modeling This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators� maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling prolongs the shelf life of the generators and prevents unexpected failures. To reduce the cost and duration of generator maintenance, these models are built with various constants, fitness functions, and objective functions. The Analytical Hierarchy Process (AHP), a decision-making tool, is implemented to aid the researcher in prioritizing and re-ranking the maintenance activities from the most important to the least. The intelligent optimization models are developed using MATLAB and the developed intelligent algorithms are tested on a case study in a coal power plant located at minjung, Perak, Malaysia. The power plant is owned and operated by Tenaga Nasional Berhad (TNB), the electric utility company in peninsular Malaysia. The results show that GA outperforms ACO since it reduces maintenance costs by 39.78% and maintenance duration by 60%. The study demonstrates that the proposed optimization method is effective in reducing maintenance time and cost while also optimizing power plant operation. � 2023 NSP Natural Sciences Publishing Cor. Final 2024-10-14T03:21:46Z 2024-10-14T03:21:46Z 2023 Article 10.18576/isl/120322 2-s2.0-85146923869 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146923869&doi=10.18576%2fisl%2f120322&partnerID=40&md5=f3dc4d6e7fcb415e6e8f1f3976fbda4c https://irepository.uniten.edu.my/handle/123456789/34690 12 3 1319 1332 Natural Sciences Publishing Scopus |
| spellingShingle | Ant-Colony Optimization Generator Genetic Algorithm Maintenance Scheduling Optimization modeling Ismail F.B. Randhawa G.S. Al-Bazi A. Alkahtani A.A. Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators |
| title | Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators |
| title_full | Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators |
| title_fullStr | Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators |
| title_full_unstemmed | Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators |
| title_short | Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators |
| title_sort | intelligent optimization systems for maintenancescheduling of power plant generators |
| topic | Ant-Colony Optimization Generator Genetic Algorithm Maintenance Scheduling Optimization modeling |
| url_provider | http://dspace.uniten.edu.my/ |