Improved Kepler Algorithm Based Maximum Power Point Tracking for Distributed Photovoltaic under Partial Shading Conditions

Affected by buildings, clouds, and dust, the power-voltage characteristic curve of distributed photovoltaic (PV) power generation system exists multi peaks, which will lead to traditional maximum power point tracking (MPPT) algorithms failing to track the global maximum power point and affect the power generation efficiency of distributed PV. To solve this problem, a MPPT control algorithm based on improved Kepler optimization algorithm is proposed in this paper. Firstly, a new convergence factor and mutation strategy are introduced into the Kepler algorithm to improve the search accuracy and tracking speed of the original Kepler algorithm. Accordingly, it can track near the global maximum power point quickly and accurately. Then, it switched to the perturb and observe method to continue tracking the determined global maximum power point, and reduces power oscillations of distributed PV. The simulation results show that the proposed algorithm in this paper has faster tracking speed and higher tracking accuracy in various working conditions compared to other existing algorithms. Finally, a hardware in the loop platform is built. It further verifies that the proposed algorithm has fast tracking speed and high tracking accuracy, and can greatly improve the power generation efficiency of distributed PV.