A Deep Recurrent Reinforcement Learning Approach for Enhanced MPPT in PV Systems

Abstract

Under partial shading condition (PSC), for efficient usage, solar photovoltaic (PV) systems are required to operate at global maximum power point (GMPP). A maximum power point tracking (MPPT) controller is usually employed for this task. In this work, for MPPT under PSC, a recurrent deep reinforcement learning (DRL) approach has been developed and investigated. The optimized neural network, augmented by long short-term memory (LSTM), is trained using the proximal policy optimization (PPO) DRL algorithm. The MPPT performance of the proposed scheme was assessed using the GMPP attainment accuracy (%), on several realistic static and dynamic test cases. The presented investigations revealed the proposed controller's average MPPT accuracy of 97.79% for the considered random static PSCs. Similarly, average MPPT accuracies of 95.69% under changing irradiance, 95.70% under change in temperature and 95.77% under changes in both irradiance and temperature were recorded. These accuracies are found to be significantly superior to recent reported works. The resulting performance enhancement is attributed to the inclusion of LSTM, which allowed the agent to retain information about past states and actions, enabling it to make informed decisions. Therefore, based on the presented investigations, it is concluded that the proposed PPO-LSTM approach is an excellent MPPT alternative for PV systems.