Reinforcement Learning Based PID Control of Wind Energy Conversion Systems



In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. The
adaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinear
characteristics of wind variations as plant input, wind turbine structure and generator operational behavior
demand for high quality adaptive controller to ensure both robust stability and safe performance. Thus, a
reinforcement learning algorithm is used for online tuning of PID coefficients in order to enhance closed loop
system performance. In this study, at start the proposed controller is applied to two pure mathematical plants,
and then the closed loop WECS behavior is discussed in the presence of a major disturbance.


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