Model-Free Optimization Scheme for Efficiency Improvement of Wind Farm Using Decentralized Reinforcement Learning⁎⁎This work was supported by the National Natural Science Foundation of China under Grants 61722307 and 5191101838

• Published in: IFAC-PapersOnLine Vol. 53; no. 2; pp. 12103 - 12108
• Main Authors: Xu, Zhiwei, Geng, Hua, Chu, Bing, Qian, Menghao, Tan, Ni
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2020
• Subjects: decentralized control; model-free approach; power optimization; Q learning method; Wind farm; model-free approach; decentralized control; Q learning method; power optimization; Wind farm
• ISSN: 2405-8963
• DOI: 10.1016/j.ifacol.2020.12.767

Wake interactions caused by the complex wakes between the turbines within a wind farm have significant adverse effect on the total power generation of the wind farm. To mitigate the effect of wake interactions and optimize the total power output of wind farm, this paper proposes a model-free control scheme using reinforcement learning by developing a decentralized Q learning method. The proposed approach guarantees that the output power of wind farm converges to the optimal total power under different wind conditions, and further ensures the gradual changes of control variables of wind turbines and thus avoids the unexpected sharp drop of the power generation performance of wind farm. Simulation results are provided to demonstrate the effectiveness of the proposed method.