Optimizing Tracking of Maximum Power in Solar Power Generation System Based on Type Two Fuzzy Controller in Partial Shading Condition Using Squirrel Search Algorithm

Today, global attention has been drawn to energy production through renewable sources, especially solar energy. Partial shading conditions have created challenging issues for maximum power point tracking, including trapping at local maximum power points, slow tracking time, and fluctuations in power output during tracking time. Therefore, in order to overcome the problems related to shading conditions and deal with local peaks, researchers have presented various hybrid techniques based on fuzzy controllers and meta-heuristic algorithms. Among all meta-heuristic algorithms, particle swarm optimization and gray wolf algorithm are the most common optimization methods due to their easy implementation, simplicity and low computational load. But the drawbacks of these algorithms are the low convergence speed and the difficulty in adjusting their parameters, and they may be caught under the rapid changes of solar radiation and shading conditions in local peaks. To deal with these problems, a new hybrid structure based on type two fuzzy controller and squirrel search algorithm has been used in this research. The type two fuzzy controller is used instead of the normal fuzzy controller and it is optimized and adjusted by the squirrel search algorithm which is used instead of the gray wolf algorithm. In this structure, by applying four radiation patterns, the effectiveness of the proposed controller has been investigated. The simulation results in MATLAB software show that the efficiency of the proposed method is about 99% on average, And using the proposed method, compared to not using it, reduces the settling time by about 36%, increases the maximum power tracking speed, and reduction of power fluctuations is possible.