Maximum Power Point Tracking of Photovoltaic System Based on Reinforcement Learning

Maximum power point tracking technique is often used in photovoltaic (PV) system to extract the maximum power at any environment condition. In this paper, a reinforcement learning based variable step size maximum power point tracking (RL MPPT) method is proposed. Q-learning is used as the algorithm of the proposed methods and is implemented by constructing the Q-table (RL-QT MPPT). A Q-network approach (RL-QN MPPT) is also proposed as a more general representation of the RL MPPT method. Implementing of the algorithm doesn't require the information of the actual PV module in advance, and the proposed system is able to track the MPP offline. With smaller ripples and faster tracking speed, the experiment results of the RL-QT MPPT method and the RL-QN MPPT method are presented.