Reinforcement learning with fuzzified reward approach for MPPT control of PV systems

Abstract The array of photovoltaic cells converts solar power into electricity and generate a clean and renewable source of energy. For them to be efficient, they must constantly generate the maximum possible power under various environmental conditions. This is the well-known problem of tracking Maximum Power Point (MPP). The classical methods fall short as they are not able to cope with the changing environmental conditions. This work aims to track MPPT by employing Deep Reinforcement Learning (RL) with a fuzzy reward mechanism introduced for better translation of continuous space into various levels of abstraction. The author simulates a model of a PV array connected to a variable load. The introduction of RL has provided an advantage as employing a Deep-Q learning algorithm makes the structure a model-free and thereby eliminates the various modeling parameters with their effect on the design. The designed setup renders 2.8 W of power under the maximum load conditions and has been able to track the maximum power under the different varying conditions of temperature and irradiations.