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dc.creatoramelec, viloria
dc.creatorNuñez Lobo, Hugo
dc.creatorPineda, Omar
dc.description.abstractRenewable 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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceIOP Conference Series: Materials Science and Engineeringspa
dc.subjectWind Turbinesspa
dc.subjectWind Fieldsspa
dc.subjectWake Effectspa
dc.subjectCombinatorial Optimizationspa
dc.subjectGenetic Algorithmsspa
dc.titleSolving the problem of optimizing wind farm design using genetic algorithmsspa
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dcterms.references[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/v9i47/107371spa
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dcterms.references[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, Chamspa
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dcterms.references[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). IEEEspa
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dcterms.references[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
dcterms.references[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, Heidelbergspa
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dcterms.references[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, 116340spa

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