A Multiagent Reinforcement Learning Approach for Wind Farm Frequency Control

Abstract

As wind turbines (WTs) become more prevalent, there is an increasing interest in actively controlling their power output to participate in the frequency regulation for the power grid. Conventional frequency regulation controllers use fixed gains, making it difficult for the WT to adjust its kinetic energy uptake to its operating conditions and to collaborate effectively with other WTs in the wind farm. In addition, the design of conventional frequency controllers does not consider their impacts on the mechanical structure. To address these issues, in this article, we model the cooperative frequency control problem for all the WTs in a wind farm as a decentralized partially observable Markov decision process and use a multiagent deep reinforcement learning algorithm to solve it. We also develop a grid-connected wind farm simulation model based on MATLAB/Simulink and OpenFAST, which can reflect the detailed interactions between the electrical and mechanical components of WTs. Simulation results show that the proposed strategy is effective in reducing frequency drops and has less impact on mechanical structure deflections compared with traditional methods.