A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach

Sara Siniscalchi-Minna, Fernando D. Bianchi, Carlos Ocampo-Martinez, Jose Luis Domínguez-García and Bart De Schutter

Renewable Energy, 2020, vol. 150, issue C, 656-669

Abstract: Owing to wake effects, the power production of each turbine in a wind farm is highly coupled to the operating conditions of the other turbines. Wind farm control strategies must take into account these couplings and produce individual power commands for each turbine. In this case, centralized control approaches might be prone to failures due to the high computational burden and communication dependency. To overcome this problem, this paper proposes a non-centralized scheme based on splitting the wind farm into almost uncoupled sets of turbines by solving a mixed-integer partitioning problem. In each set of turbines, a model predictive control strategy seeks to optimize the distribution of the power set-points among turbines such that the impact of the power losses due to the wake effect is reduced. Then, a supervisory controller coordinates the generation of each group to satisfy the power demanded by the grid operator. The effectiveness of the proposed control scheme in terms of reduction of computational costs and power regulation is confirmed by simulations for a wind farm of 42 turbines.

Keywords: Wind farm control; Non-centralized control; Partitioning algorithms; Model predictive control; Wake effect (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:150:y:2020:i:c:p:656-669

DOI: 10.1016/j.renene.2019.12.139

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