基于旗鱼优化算法与扰动观察法复合控制的最大功率点跟踪策略

Maximum Power Point Tracking Strategy Based on Hybrid Control of Sailfish Optimization Algorithm and Perturbation and Observation Method

针对传统的最大功率点跟踪MPPT(maximum power point tracking)方法在部分遮阴条件下陷入局部最优而失效,且常见的智能优化算法往往存在收敛精度差、收敛速度慢、系统稳定性不高等问题,提出1种基于旗鱼优化SFO(sailfish optimization)算法与扰动观察P&O(perturbation and observation)法混合控制的光伏系统最大功率跟踪策略。SFO算法同时使用旗鱼(捕食者)和沙丁鱼(猎物)2个种群,可保证粒子在全局空间探索。所提混合算法先利用SFO算法快速跟踪到最大功率点附近,再利用小步长P&O法对最大功率点进行精细搜索,最后利用分段步长的方法同时兼顾MPPT搜索速度和搜索精度的要求。仿真结果表明,所提混合控制策略有效提升了控制系统的响应速度及跟踪精度,提升了系统的稳定性。

The traditional maximum power point tracking(MPPT)method is prone to falling into a local optimum under partial shading conditions and failing,while the common intelligent optimization algorithms often have disadvantages such as a low convergence accuracy,a slow convergence speed,and a low system stability.Aimed at these problems,a maximum power tracking strategy for photovoltaic(PV)system is proposed,which is based on the hybrid control of sailfish optimization(SFO)algorithm and perturbation and observation(P&O)method.The SFO algorithm uses two populations of sailfish(predator)and sardine(prey)at the same time to ensure the exploration of particles in the global space.The hybrid algorithm uses the SFO algorithm to quickly track the neighborhood of maximum power point at first,and then it uses the P&O method with a small step size to finely search for the maximum power point.In this way,it takes advantage of the piecewise step method to meet the requirements of MPPT search speed and search accuracy.Simulation results show that the hybrid control strategy effectively improves the response speed and tracking accuracy of the control system,as well as its stability.