A hybrid RBNN-PID control and tracking the global maximum power point of PV solar system under partial shading

In Photovoltaic Systems, Maximum Power Point Tracking (MPPT) is an essential research goal, because these systems are very big in size and may be insecure in daily changeable environments. The design of most traditional MPPT methods is developed to track the Maximum Power Point (MPP) for stable and uniform environments, which is considered as a single MPP. This research proposes a hybrid controller composed of a radial basis neural network and Proportional-Integral-Derivative controller to improve the performance and response of the boost converter. The controller is designed to enable the Photovoltaic solar system to work at the global maximum power point for different cases of partial shading and temperature variations. The results showed that the proposed method is effective and works properly. The comparison results showed that the proposed system outperforms these systems in tracking speed, accuracy, and stability during fluctuations in environmental conditions. The longest tracking time is 0.095s, and the lowest efficiency is 98%. These advantages offer that the proposed system is promising compared with the most recent related works.