Solving the problem of optimizing wind farm design using genetic algorithms
Renewable energies have become a topic of great interest in recent years because the natural sources used for the generation of these energies are inexhaustible and non-polluting. In fact, environmental sustainability requires a considerable reduction in the use of fossil fuels, which are highly pol...
- Autores:
-
amelec, viloria
Nuñez Lobo, Hugo
Pineda, Omar
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7967
- Acceso en línea:
- https://hdl.handle.net/11323/7967
https://doi.org/10.1088/1757-899X/872/1/012029
https://repositorio.cuc.edu.co/
- Palabra clave:
- Wind Turbines
Wind Fields
Wake Effect
Combinatorial Optimization
Genetic Algorithms
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Solving the problem of optimizing wind farm design using genetic algorithms |
| title |
Solving the problem of optimizing wind farm design using genetic algorithms |
| spellingShingle |
Solving the problem of optimizing wind farm design using genetic algorithms Wind Turbines Wind Fields Wake Effect Combinatorial Optimization Genetic Algorithms |
| title_short |
Solving the problem of optimizing wind farm design using genetic algorithms |
| title_full |
Solving the problem of optimizing wind farm design using genetic algorithms |
| title_fullStr |
Solving the problem of optimizing wind farm design using genetic algorithms |
| title_full_unstemmed |
Solving the problem of optimizing wind farm design using genetic algorithms |
| title_sort |
Solving the problem of optimizing wind farm design using genetic algorithms |
| dc.creator.fl_str_mv |
amelec, viloria Nuñez Lobo, Hugo Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Nuñez Lobo, Hugo Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Wind Turbines Wind Fields Wake Effect Combinatorial Optimization Genetic Algorithms |
| topic |
Wind Turbines Wind Fields Wake Effect Combinatorial Optimization Genetic Algorithms |
| description |
Renewable energies have become a topic of great interest in recent years because the natural sources used for the generation of these energies are inexhaustible and non-polluting. In fact, environmental sustainability requires a considerable reduction in the use of fossil fuels, which are highly polluting and unsustainable [1]. In addition, serious environmental pollution is threatening human health, and many public concerns have been raised [2]. As a result, many countries have proposed ambitious plans for the production of green energy, including wind power, and consequently, the market for wind energy is expanding rapidly worldwide [3]. In this research, an evolutionary metaheuristic is implemented, specifically genetic algorithms. |
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2020 |
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2020-09-15 |
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2021-03-08T19:13:02Z |
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2021-03-08T19:13:02Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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17578981 1757899X |
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https://hdl.handle.net/11323/7967 |
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https://doi.org/10.1088/1757-899X/872/1/012029 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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17578981 1757899X Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
[1] Mittal, P., & Mitra, K. (2020). Efficient Wind Farm Micro-siting using Novel Optimization Approaches (Doctoral dissertation, Indian Institute of Technology Hyderabad). [2] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371 [3] Ryerkerk, M. L., Averill, R. C., Deb, K., & Goodman, E. D. (2017). Solving metameric variable-length optimization problems using genetic algorithms. Genetic Programming and Evolvable Machines, 18(2), 247-277 [4] Moreno-Carbonell, S., Sánchez-Úbeda, E. F., & Muñoz, A. (2020). Rethinking weather station selection for electric load forecasting using genetic algorithms. International Journal of Forecasting, 36(2), 695-712. [5] Li, Q. S., Liu, D. K., Fang, J. Q., & Tam, C. M. (2000). Multi-level optimal design of buildings with active control under winds using genetic algorithms. Journal of Wind Engineering and Industrial Aerodynamics, 86(1), 65-86 [6] Rinaldi, G., Pillai, A. C., Thies, P. R., & Johanning, L. (2019). Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms. Wind Engineering, 0309524X19849826 [7] Sanchez, L., Vásquez, C., & Viloria, A. (2018, June). Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In International Conference on Data Mining and Big data (pp. 759- 766). Springer, Cham [8] Diveux, T., Sebastian, P., Bernard, D., Puiggali, J. R., & Grandidier, J. Y. (2001). Horizontal axis wind turbine systems: optimization using genetic algorithms. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 4(4), 151-171 [9] Garcia, J., Khosravi, A., Poley, R., Assad, M., & Machado, L. (2019, March). Multiobjective optimization of air conditioning system with the low GWP refrigerant R1234yf using genetic algorithm. In 2019 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-7). IEEE [10] Abdelsalam, A. M., & El-Shorbagy, M. A. (2018). Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renewable Energy, 123, 748- 755. [11] Thejaswini, R., & Raju, H. P. (2018, February). Optimizing Wind Turbine-Generator Design Using Genetic Algorithm. In 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) (pp. 1-5). IEEE [12] Guerrero, M., Montoya, F. G., Baños, R., Alcayde, A., & Gil, C. (2018). Community detection in national-scale high voltage transmission networks using genetic algorithms. Advanced Engineering Informatics, 38, 232-241. [13] Tao, S., Xu, Q., Feijoo, A., Hou, P., & Zheng, G. (2020). Bi-hierarchy optimization of a wind farm considering environmental impact. IEEE Transactions on Sustainable Energy. [14] Wan, C., Wang, J., Yang, G., & Zhang, X. (2010, June). Optimal micro-siting of wind farms by particle swarm optimization. In International Conference in Swarm Intelligence (pp. 198-205). Springer, Berlin, Heidelberg [15] Daneshfar, F., & Bevrani, H. (2012). Multiobjective design of load frequency control using genetic algorithms. International Journal of Electrical Power & Energy Systems, 42(1), 257-263. [16] Song, M., Chen, K., & Wang, J. (2020). A two-level approach for three-dimensional micro-siting optimization of large-scale wind farms. Energy, 190, 116340 |
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amelec, viloria2f22a05451ff1bbfc2d4dd00035c952fNuñez Lobo, Hugo0f41252ecf86ab7c467a63f45e2f1297300Pineda, Omaraf4b322b3d3157067b1e466da357fb982021-03-08T19:13:02Z2021-03-08T19:13:02Z2020-09-15175789811757899Xhttps://hdl.handle.net/11323/7967https://doi.org/10.1088/1757-899X/872/1/012029Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Renewable energies have become a topic of great interest in recent years because the natural sources used for the generation of these energies are inexhaustible and non-polluting. In fact, environmental sustainability requires a considerable reduction in the use of fossil fuels, which are highly polluting and unsustainable [1]. In addition, serious environmental pollution is threatening human health, and many public concerns have been raised [2]. As a result, many countries have proposed ambitious plans for the production of green energy, including wind power, and consequently, the market for wind energy is expanding rapidly worldwide [3]. In this research, an evolutionary metaheuristic is implemented, specifically genetic algorithms.application/pdfengCorporación Universidad de la CostaRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2IOP Conference Series: Materials Science and Engineeringhttps://iopscience.iop.org/article/10.1088/1757-899X/872/1/012194Wind TurbinesWind FieldsWake EffectCombinatorial OptimizationGenetic AlgorithmsSolving the problem of optimizing wind farm design using genetic algorithmsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Mittal, P., & Mitra, K. (2020). Efficient Wind Farm Micro-siting using Novel Optimization Approaches (Doctoral dissertation, Indian Institute of Technology Hyderabad).[2] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371[3] Ryerkerk, M. L., Averill, R. C., Deb, K., & Goodman, E. D. (2017). Solving metameric variable-length optimization problems using genetic algorithms. Genetic Programming and Evolvable Machines, 18(2), 247-277[4] Moreno-Carbonell, S., Sánchez-Úbeda, E. F., & Muñoz, A. (2020). Rethinking weather station selection for electric load forecasting using genetic algorithms. International Journal of Forecasting, 36(2), 695-712.[5] Li, Q. S., Liu, D. K., Fang, J. Q., & Tam, C. M. (2000). Multi-level optimal design of buildings with active control under winds using genetic algorithms. Journal of Wind Engineering and Industrial Aerodynamics, 86(1), 65-86[6] Rinaldi, G., Pillai, A. C., Thies, P. R., & Johanning, L. (2019). Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms. Wind Engineering, 0309524X19849826[7] Sanchez, L., Vásquez, C., & Viloria, A. (2018, June). Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In International Conference on Data Mining and Big data (pp. 759- 766). Springer, Cham[8] Diveux, T., Sebastian, P., Bernard, D., Puiggali, J. R., & Grandidier, J. Y. (2001). Horizontal axis wind turbine systems: optimization using genetic algorithms. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 4(4), 151-171[9] Garcia, J., Khosravi, A., Poley, R., Assad, M., & Machado, L. (2019, March). Multiobjective optimization of air conditioning system with the low GWP refrigerant R1234yf using genetic algorithm. In 2019 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1-7). IEEE[10] Abdelsalam, A. M., & El-Shorbagy, M. A. (2018). Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renewable Energy, 123, 748- 755.[11] Thejaswini, R., & Raju, H. P. (2018, February). Optimizing Wind Turbine-Generator Design Using Genetic Algorithm. In 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) (pp. 1-5). IEEE[12] Guerrero, M., Montoya, F. G., Baños, R., Alcayde, A., & Gil, C. (2018). Community detection in national-scale high voltage transmission networks using genetic algorithms. Advanced Engineering Informatics, 38, 232-241.[13] Tao, S., Xu, Q., Feijoo, A., Hou, P., & Zheng, G. (2020). Bi-hierarchy optimization of a wind farm considering environmental impact. IEEE Transactions on Sustainable Energy.[14] Wan, C., Wang, J., Yang, G., & Zhang, X. (2010, June). Optimal micro-siting of wind farms by particle swarm optimization. In International Conference in Swarm Intelligence (pp. 198-205). Springer, Berlin, Heidelberg[15] Daneshfar, F., & Bevrani, H. (2012). Multiobjective design of load frequency control using genetic algorithms. International Journal of Electrical Power & Energy Systems, 42(1), 257-263.[16] Song, M., Chen, K., & Wang, J. (2020). A two-level approach for three-dimensional micro-siting optimization of large-scale wind farms. Energy, 190, 116340CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstream/11323/7967/2/license_rdf42fd4ad1e89814f5e4a476b409eb708cMD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstream/11323/7967/3/license.txte30e9215131d99561d40d6b0abbe9badMD53open accessORIGINALSolving the problem of optimizing wind farm design using genetic.pdfSolving the problem of optimizing wind farm design using genetic.pdfapplication/pdf2152123https://repositorio.cuc.edu.co/bitstream/11323/7967/1/Solving%20the%20problem%20of%20optimizing%20wind%20farm%20design%20using%20genetic.pdf2d79f80ea2b4d2540748fd4dd67e1b8fMD51open accessTHUMBNAILSolving the problem of optimizing wind farm design using genetic.pdf.jpgSolving the problem of optimizing wind farm design using 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