Parameter optimization and maximum power point tracking control of photo-voltaic system based on the hybrid reinforcement learning whale optimization algorithm

Meta-heuristic algorithms are a class of optimization algorithms that are commonly used to solve complex optimization problems, such as the optimization of the performance of MPPT (Maximum Power Point Tracking) control in Photovoltaic (PV) systems. Meta-heuristic algorithms are flexible and adaptable, and they can be applied to a wide range of optimization problems without the need for extensive knowledge of the specific characteristics of the problem. In the context of MPPT control, meta-heuristic algorithms can be used to optimize the performance of the MPPT algorithm by finding the optimal operating point of the PV cells that maximizes the power output of the PV array. This is typically a complex optimization problem, as the maximum power point depends on various factors, such as the intensity and temperature of the sunlight, and these factors can change over time. Meta-heuristic algorithms are well-suited for this type of optimization problem, as they can search for the optimal solution in a flexible and efficient manner. Overall, the use of meta-heuristic algorithms in optimizing the performance of MPPT control in PV systems can help to improve the efficiency and reliability of the PV system, and it can make the MPPT control algorithm more effective and adaptive to changing operating conditions. This can help to make PV systems more competitive and cost-effective as a source of renewable energy. Therefore in the current study, we propose to use a reinforcement learning based Whale optimization algorithm (RL-WOA) to parameter learning of PV systems first. Then we extend the idea to optimize the performance of the MPPT by modifying the duty cycle of a boost converter in the structure of a PV system model, to get the maximum available output energy.