A novel control strategy approach to optimally design a wind farm layout

Wang, Longyan, Tan, Andy, & Gu, YuanTong (2016) A novel control strategy approach to optimally design a wind farm layout. Renewable Energy, 95, pp. 10-21.

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Recently wind energy has become one of the most important alternative energy sources and is growing at a rapid rate because of its renewability and abundancy. For the clustered wind turbines in a wind farm, significant wind power losses have been observed due to wake interactions of the air flow induced by the upstream turbines to the downstream turbines. One approach to reduce power losses caused by the wake interactions is through the optimization of wind farm layout, which determine the wind turbine positions and control strategy, which determine the wind turbine operations. In this paper, a new approach named simultaneous layout plus control optimization is developed. The effectiveness is studied by comparison to two other approaches (layout optimization and control optimization). The results of different optimizations, using both grid based and unrestricted coordinate wind farm design methods, are compared for both ideal and realistic wind conditions. Even though the simultaneous layout plus control optimization is theoretically superior to the others, it is prone to the local minima. Through the parametric study of crossover and mutation probabilities of the optimization algorithm, the results of the approach are generally satisfactory. For both simple and realistic wind conditions, the wind farm with the optimized control strategy yield 1–3 kW more power per turbine than that with the self-optimum control strategy, and the unrestricted coordinate method yield 1–2 kW more power per turbine than the grid based method.

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ID Code: 99549
Item Type: Journal Article
Refereed: Yes
Keywords: Layout optimization, Control optimization, Self-optimum control strategy, Optimized control strategy
DOI: 10.1016/j.renene.2016.03.104
ISSN: 0960-1481
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Copyright Owner: Copyright 2016 Elsevier
Copyright Statement: Licensed under the Creative Commons Attribution; Non-Commercial; No-Derivatives 4.0 International. DOI: 10.1016/j.renene.2016.03.104
Deposited On: 27 Sep 2016 04:40
Last Modified: 04 Oct 2016 22:54

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