局部遮荫下基于IP&O-SSA的MPPT控制研究

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

为了解决传统最大功率点跟踪(maximum power point tracking,MPPT)控制算法在局部遮荫环境中易陷入局部最优的问题,以及智能优化算法寻优速度慢的问题,提出了一种基于自适应扰动观察(IP&O)和改进麻雀搜索算法(sparrow search algorithm,SSA)的复合IP&O-SSA。该算法对SSA加入了Tent序列初始化,对预警者加入了Levy飞行策略,再对P&O进行了自适应和滤波处理。该算法采用双层控制结构,先通过改进后的SSA进行全局搜索到最大功率点附近,再通过改进后的IP&O进行小步平缓搜索到跟踪最大功率点。通过在Simulink仿真标准环境、局部遮荫、环境突变3种情形,仿真结果表明:在标准环境下,该算法最先跟踪到最大功率点,收敛时间比改进前的扰动观察(P&O)和SSA缩短了3 ms、16 ms,跟踪效率高达99.99%;局部遮荫条件下,只有P&O会陷入局部最优,无法有效跟踪到系统的最大功率点,相较于改进前的SSA,该文算法的平均收敛时间缩短了8 ms,同时跟踪效率高达99.68%,提升了0.09%。验证了该算法适用于日常大部分应用情景,为提升光伏阵列的发电效率提供了理论控制算法基础,为之后的光伏阵列并网减少了不必要的功率损耗。

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.