Deep Reinforcement Learning Based Optimal Perturbation for MPPT in Photovoltaics

Abstract

Methods to draw maximum power from Photovoltaic (PV) modules are an ongoing research topic. The socalled Maximum Power Point Tracking (MPPT) method aims to operate the PV module at its maximum power point (MPP) by matching the load resistance to its characteristic resistance, which changes with temperature and solar irradiance. Perturbation and Observation (P&O) is a popular method that lays the foundation for many advanced techniques. We propose a deep reinforcement learning (RL) based algorithm to determine the optimal perturbation size to reach the MPP. Our method utilizes an artificial neural network-based predictor to determine the MPP from temperature and solar irradiance measurements. The proposed technique provides an effective learning-based solution to the classical MPPT problem. The effectiveness of our model is demonstrated through comparative analysis with respect to the popular methods from the literature.