Research on MPPT control based on IP&O-SSA under partial shading

Abstract

Traditional maximum power point tracking (MPPT) control algorithms easily fall into local optima in local shaded environments, and the speed of intelligent optimization algorithms is slow. A composite IP&O-SSA based on adaptive perturbation observation (IP&O) and improved sparrow search algorithm (SSA) was proposed. This algorithm adds Tent sequence initialization to SSA and Levy flight strategy to early warning personnel, and further adaptive and filtering processing was performed on P&O. The algorithm adopts a double-layer control structure, which first searches globally near the maximum power point through the improved SSA, and then slowly searches to track the maximum power point through the improved IP&O. By simulating three scenarios in Simulink:standard environment, local shading, and sudden environmental changes, the simulation results show that in the standard environment, the algorithm first tracks the maximum power point, and the convergence time is shortened by 3 ms and 16 ms compared to the improved perturbation observation(P&O) and SSA, with a tracking efficiency of 99.99%, and under local shading conditions, only the P&O method falls into local optima and cannot effectively track the maximum power point of the system. Compared with the improved SSA, the average convergence time of the algorithm in this paper is shortened by 8 ms, and the tracking efficiency is as high as 99.68%, which is 0.09% higher. It is verified that the algorithm is applicable to most daily application scenarios, providing a theoretical control algorithm basis for improving the power generation efficiency of Photovoltaic system, and reducing unnecessary power loss for the subsequent grid connection of Photovoltaic system.

Keywords

• partial shaded condition
• maximum power point tracking
• sparrow search algorithm
• perturbation and observation method
• photovoltaic module