Maximum Power Point Tracking of Photovoltaic Systems Using Deep Q-networks
A photovoltaic (PV) generator exhibits nonlinear current-voltage characteristics and its maximum power point varies with incident atmospheric conditions. Therefore, maximum power point tracking (MPPT) control is required to maximize the output power of the PV generator. In this paper, deep Q-network based reinforcement learning strategy is proposed to optimize MPPT process for the photovoltaic system. The proposed system uses a novel control method which introduces agent to interface with the environment and finally gets the strategy of maximum reward accordingly. Simulations and experiments show the feasibility and effectiveness of the proposed system. Compared with the traditional perturb and observe (PO) and incremental conductance (InC) methods, this method prominently saves tracking steps.
