GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization
Wind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind far...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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University of Belgrade - Faculty of Mechanical Engineering, Belgrade
2023-01-01
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| Series: | FME Transactions |
| Subjects: | |
| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2023/1451-20922304575M.pdf |
| _version_ | 1827589246832082944 |
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| author | Mohandes Mohamed Khan Salman A. Rehman Shafiqur Al-Shaikhi Ali Liu Bo Iqbal Kashif |
| author_facet | Mohandes Mohamed Khan Salman A. Rehman Shafiqur Al-Shaikhi Ali Liu Bo Iqbal Kashif |
| author_sort | Mohandes Mohamed |
| collection | DOAJ |
| description | Wind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind farm layout design problem has traditionally been solved using nature-inspired algorithms. An important issue in nature-inspired algorithms is the termination condition, which governs the execution time of the algorithm. To optimize the execution time, appropriate termination conditions should be employed. This study proposes the concept of a rewarding mechanism to achieve optimization in termination conditions while maintaining the solution quality. The proposed rewarding mechanism, adopted from the stochastic evolution algorithm, is incorporated into a genetic algorithm. The proposed genetic algorithm with the rewarding mechanism (GARM) is empirically tested using real data from a potential wind farm site with different rewarding iterations. |
| first_indexed | 2024-03-09T00:52:57Z |
| format | Article |
| id | doaj.art-ce892bfaee664a8c82d7d2be11f56ff9 |
| institution | Directory Open Access Journal |
| issn | 1451-2092 2406-128X |
| language | English |
| last_indexed | 2024-03-09T00:52:57Z |
| publishDate | 2023-01-01 |
| publisher | University of Belgrade - Faculty of Mechanical Engineering, Belgrade |
| record_format | Article |
| series | FME Transactions |
| spelling | doaj.art-ce892bfaee664a8c82d7d2be11f56ff92023-12-11T17:05:24ZengUniversity of Belgrade - Faculty of Mechanical Engineering, BelgradeFME Transactions1451-20922406-128X2023-01-0151457558410.5937/fme2304575M1451-20922304575MGARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimizationMohandes Mohamed0Khan Salman A.1Rehman Shafiqur2Al-Shaikhi Ali3Liu Bo4Iqbal Kashif5King Fahd University of Petroleum & Minerals, Electrical Engineering Department, Dhahran, Saudi ArabiaCollege of Computing & Info, Sciences Karachi, Institute of Economics and Technology, Karachi, PakistanKing Fahd University of Petroleum & Minerals, Research Institute, Center for Engineering Research, Dhahran, Saudi ArabiaKing Fahd University of Petroleum & Minerals, Electrical Engineering Department, Dhahran, Saudi ArabiaKing Fahd University of Petroleum & Minerals, Electrical Engineering Department, Dhahran, Saudi ArabiaCollege of Computing & Info, Sciences Karachi, Institute of Economics and Technology, Karachi, PakistanWind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind farm layout design problem has traditionally been solved using nature-inspired algorithms. An important issue in nature-inspired algorithms is the termination condition, which governs the execution time of the algorithm. To optimize the execution time, appropriate termination conditions should be employed. This study proposes the concept of a rewarding mechanism to achieve optimization in termination conditions while maintaining the solution quality. The proposed rewarding mechanism, adopted from the stochastic evolution algorithm, is incorporated into a genetic algorithm. The proposed genetic algorithm with the rewarding mechanism (GARM) is empirically tested using real data from a potential wind farm site with different rewarding iterations.https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2023/1451-20922304575M.pdfwind farm layout designwind farm micrositingwind energyoptimizationstochastic evolutionartificial intelligencenature-inspired algorithmsgenetic algorithms |
| spellingShingle | Mohandes Mohamed Khan Salman A. Rehman Shafiqur Al-Shaikhi Ali Liu Bo Iqbal Kashif GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization FME Transactions wind farm layout design wind farm micrositing wind energy optimization stochastic evolution artificial intelligence nature-inspired algorithms genetic algorithms |
| title | GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization |
| title_full | GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization |
| title_fullStr | GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization |
| title_full_unstemmed | GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization |
| title_short | GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization |
| title_sort | garm a stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization |
| topic | wind farm layout design wind farm micrositing wind energy optimization stochastic evolution artificial intelligence nature-inspired algorithms genetic algorithms |
| url | https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2023/1451-20922304575M.pdf |
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