An Adaptive Network-Based Reinforcement Learning Method for {MPPT} Control of {PMSG} Wind Energy Conversion Systems

This paper proposes an artificial neural network (ANN)-based reinforcement learning (RL) maximum power point tracking (MPPT) algorithm for permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs). The proposed MPPT algorithm first learns the optimal relationship between the rotor speed and electrical power of the PMSG through a combination of the ANNs and the Q-learning method. The MPPT algorithm is switched from the online RL to the optimal relation-based online MPPT when the maximum power point is learned. The proposed online learning algorithm enables the WECS to behave like an intelligent agent with memory to learn from its own experience, thus improving the learning efficiency. The online RL process can be reactivated any time when the actual optimal relationship deviates from the learned one due to the aging of the system or a change in the environment. Simulation and experimental results are provided to validate the proposed ANN-based RL MPPT control algorithm for a 5-MW PMSG-based WECS and a small emulated PMSG-based WECS, respectively.