Comparison of Deep Reinforcement Learning Techniques with Gradient based approach in Cooperative Control of Wind Farm

Abstract

The control settings of a turbines play a major role in increasing the energy production from a wind farm. The nonlinear interactions of wake between the turbines make optimal control of wind farm a challenging task. Therefore, it's hard to find the proper model based method to optimize the control settings. In the recent years, Reinforcement Learning (RL) has been emerging as a promising method for wind farm control. However, its efficacy is not evaluated when compared with nonlinear control strategies. In this study, yaw misalignment is used as control parameter to deflect the wakes and increase the power production from a 4×4 wind farm. A model-free Deep Deterministic Policy Gradient (DDPG) method and model-based iterative Linear Quadratic Regulator (iLQR) based Reinforcement Learning Techniques are utilized to optimize the yaw misalignments. To prove the efficiency of RL techniques, the results of DDPG and iLQR are compared with a nonlinear cooperative control strategy, Maximum Power Point Tracking solved through gradient based optimization approach.