A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array

Abstract

A reinforcement learning-based maximum power point tracking (RLMPPT) method is proposed for photovoltaic (PV) array. By utilizing the developed system model of PV array and configuring the environment for the reinforcement learning, the proposed RLMPPT method is able to observe the environment state of the PV array in the learning process and to autonomously adjust the perturbation to the operating voltage of the PV array in obtaining the best MPP. Simulations of the proposed RLMPPT for a PV array are conducted. Experimental results demonstrate that, in comparison to an existing MPPT method, the RLMPPT not only achieves better efficiency factor for both simulated weather data and real weather data but also adapts to the environment much fast with very short learning time.