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|Title:||Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization||Authors:||Biswas, Partha Pratim
Suganthan, Ponnuthurai Nagaratnam
Amaratunga, Gehan A. J.
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2017||Source:||Biswas, P. P., Suganthan, P. N., & Amaratunga, G. A. J. (2018). Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization. Renewable Energy, 115, 326-337. doi:10.1016/j.renene.2017.08.041||Journal:||Renewable Energy||Abstract:||An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning facilities for erection, installation and future maintenance. This paper proposes an approach where several options of optimized usable windfarm layouts can be obtained in a single run of decomposition based multi-objective evolutionary algorithm (MOEA/D). A set of Pareto optimal vectors is obtained with objective as maximum output power at minimum wake loss i.e. at maximum efficiency. Maximization of both output power and windfarm efficiency are set as two objectives for optimization. The objectives thus formulated ensure that in any single Pareto optimal solution the number of turbines used are placed at most optimum locations in the windfarm to extract maximum power available in the wind. Case studies with actual manufacturer data for wind turbines of same as well as different hub heights and with realistic wind data are performed under the scope of this research study.||URI:||https://hdl.handle.net/10356/139921||ISSN:||0960-1481||DOI:||10.1016/j.renene.2017.08.041||Rights:||© 2017 Elsevier Ltd. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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