A Data-Driven, Cooperative Approach for Wind Farm Control: A Wind Tunnel Experimentation

Jinkyoo Park, Soon-Duck Kwon and Kincho Law
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Jinkyoo Park: Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
Soon-Duck Kwon: Department of Civil Engineering, Chonbuk National University, Jeonju 5896, Korea
Kincho Law: Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA

Energies, 2017, vol. 10, issue 7, 1-17

Abstract: This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions between the wind turbines in a wind farm. Because of wake interference, such greedy control strategy can significantly lower the power production of the downstream wind turbines and, thus, reduce the overall wind farm power production. As an alternative to the greedy control strategy, we study a cooperative wind farm control strategy that determines and executes the optimum coordinated control actions for maximizing the total wind farm power production. To determine the optimum coordinated control actions of the wind turbines, we employ a data-driven optimization method that seeks to find the optimum control actions using only the power measurement data collected from the wind turbines in a wind farm. In particular, we employ the Bayesian Ascent (BA) algorithm, a probabilistic optimization method constructed based on Gaussian Process regression and the trust region concept. Wind tunnel experiments using 6 scaled wind turbine models are conducted to assess (1) the effectiveness of the cooperative control strategy in improving the power production; and (2) the efficiency of the BA algorithm in determining the optimum control actions of the wind turbines using only the input control actions and the output power measurement data.

Keywords: wind farm control; Bayesian Ascent algorithm; wind tunnel experiment; data-driven optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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