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Open AccessArticle

Data-Driven Decentralized Algorithm for Wind Farm Control with Population-Games Assistance

1
Learning & Game Theory Laboratory, New York University Abu Dhabi, Saadiyat Campus, P.O. Box 129188, Abu Dhabi 129188, UAE
2
Automatic Control Department, Universitat Politècnica de Catalunya, Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens i Artigas, 4-6, 08028 Barcelona, Spain
3
Instituto Balseiro and CONICET, Av. Bustillo 9500, San Carlos de Bariloche 8400, Argentina
4
Departamento de Ingeniería Eléctrica y Electrónica, Universidad de los Andes, Carrera 1A No 18A-10, Bogotá 111711, Colombia
*
Authors to whom correspondence should be addressed.
Energies 2019, 12(6), 1164; https://doi.org/10.3390/en12061164
Received: 5 February 2019 / Revised: 15 March 2019 / Accepted: 21 March 2019 / Published: 26 March 2019
(This article belongs to the Special Issue Control Schemes for Wind Electricity Systems)
In wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines is high. In order to deal with these issues, there is a need to develop control strategies that maximize the energy captured from a wind farm. In this work, an algorithm that uses multiple estimated gradients based on measurements that are classified by using a simple distributed population-games-based algorithm is proposed. The update in the decision variables is computed by making a superposition of the estimated gradients together with the classification of the measurements. In order to maximize the energy captured and maintain the individual power generation, several constraints are considered in the proposed algorithm. Basically, the proposed control scheme reduces the communications needed, which increases the reliability of the wind farm operation. The control scheme is validated in simulation in a benchmark corresponding to the Horns Rev wind farm. View Full-Text
Keywords: data-driven control strategy; wind turbines data-driven control strategy; wind turbines
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Barreiro-Gomez, J.; Ocampo-Martinez, C.; Bianchi, F.D.; Quijano, N. Data-Driven Decentralized Algorithm for Wind Farm Control with Population-Games Assistance. Energies 2019, 12, 1164.

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