A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Data-driven decentralized algorithm for wind farm control with population-games assistance
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. ; This work has been partially funded by the Spanish projects SCAV (Ref. DPI2017-88403-R) and DEOCS (Ref. DPI2016-76493-C3-3-R) and the Colombian project ISAGEN Solución Energética Piloto La Guajira. J. Barreiro-Gomez gratefully acknowledges support from U.S. Air Force Office of Scientific Research under grant number FA9550-17-1-0259.
Data-driven decentralized algorithm for wind farm control with population-games assistance
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. ; This work has been partially funded by the Spanish projects SCAV (Ref. DPI2017-88403-R) and DEOCS (Ref. DPI2016-76493-C3-3-R) and the Colombian project ISAGEN Solución Energética Piloto La Guajira. J. Barreiro-Gomez gratefully acknowledges support from U.S. Air Force Office of Scientific Research under grant number FA9550-17-1-0259.
Data-driven decentralized algorithm for wind farm control with population-games assistance
Barreiro-Gómez, Julian (author) / Ocampo-Martinez, Carlos (author) / Bianchi, Fernando D. (author) / Quijano, Nicanor (author) / Ministerio de Ciencia, Innovación y Universidades (España) / Ministerio de Economía y Competitividad (España) / Agencia Estatal de Investigación (España) / Air Force Office of Scientific Research (US) / Isagen
2019-03-26
Article (Journal)
Electronic Resource
English
DDC:
690
| American Institute of Physics | 2014
Wind turbine positioning optimization of wind farm using greedy algorithm
| American Institute of Physics | 2013
Complete Decentralized Displacement Control Algorithm
| British Library Conference Proceedings | 2007
Wind farm layout optimization using imperialist competitive algorithm
| American Institute of Physics | 2014