Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models

In photovoltaic (PV) model, it is an urgent problem to control and optimize the accurate parameters. Hence, many algorithms have been proposed for parameter extraction of different PV models. However, the ability of many optimization algorithms is greatly affected by parameters, and the same parameter is not suitable for different model problems. In recent years, reinforcement learning has achieved competitive results in solving the problem of maximizing returns through learning strategies in the process of interaction with the environment. Therefore, in this paper we propose a new algorithm which combines differential evolution algorithm with reinforcement learning. Specifically, in the iterative process, the fitness function value is evaluated to determine the action reward for adjustment of parameter value, and the parameter value is adjusted through reinforcement learning to obtain the most suitable algorithm parameters for the environment model. The performance of the proposed approach has been verified by extracting single diode model, double diode model and PV module parameters, The simulation results (root mean square error) of single diode model (9.8602E−04), double diode model (9.8248E−04) and 2.4251E−03 for the Photowatt-PWP201, 1.7298E−03 for STM6-40/36 and 1.6601E−02 for the STP6-120/36 comprehensively show that the algorithm has better accuracy and robustness when compared with other advanced algorithms.