Analyses of Deep Reinforcement Learning and Conventional MPPT Control under Fast-Changing Irradiance

Abstract

This paper presents the analyses of a deep reinforcement learning, DRL approach for maximum power point tracking (MPPT) of a photovoltaic PV boost converter system. The DRL controller used is the Deep Deterministic Policy Gradient (DDPG) algorithm, which is designed to address the limitations of conventional perturb and observe, P&O methods with PID control, such as reduced efficiency under non – uniform irradiance or load variations. The DDPG framework offers an adaptive and computationally efficient alternative by learning the nonlinear mapping between PV states and duty cycle control. Thus, a comparative study of the DDPG with the conventional approach is performed under non – uniform irradiance and varying load resistance. Parameters of power and voltage were evaluated as well as the oscillatory performance for both controllers. The outcome of the results shows that the DDPG controller can achieve higher power extraction with significantly reduced under higher load impedances The conventional approach, however, achieves a better performance under uniform irradiation and low resistance conditions.  This is due to the absence of multiple peaks that allows the conventional approach to rapidly converge to the MPP, while the exploratory nature DDPG introduces minor bias due to its exploratory learning process. These findings highlight the potential of DDPG to enhance the efficiency and adaptability of MPPT, particularly relevant in standalone DC microgrid applications.