基于飞蛾扑火优化算法的光伏阵列MPPT研究  

Maximum Power Point Tracking for Photovoltaic System Under Partial Shading Condition Using Moth-flame Optimization Algorithm

作　　者：薛飞 张登雨 石季英[2] 周雷 XUE Fei;ZHANG Deng-yu;SHI Ji-ying;ZHOU Lei(Ningxia Key Laboratory of Electric Energy Security,State Grid Ningxia Electric Power Company Electric Power Research Institute,Yinchuan 750002,China)

机构地区：[1]宁夏电力能源安全重点实验室,国网宁夏电力有限公司电力科学研究院,宁夏银川750002 [2]天津大学智能电网教育部重点实验室,天津300072

出　　处：《电力电子技术》2019年第5期60-63,共4页Power Electronics

基　　金：国家高技术研究发展计划(2015AA050202);国家自然科学基金(61571324)~~

摘　　要：飞蛾扑火优化(MFO)算法是一种新型智能算法,具有局部避免和快速收敛能力,并且可以很好地协调全局搜索和局部搜索。针对光伏(PV)阵列在局部阴影条件(PSC)下其输出特性曲线呈现多峰的情况,此处提出了一种基于MF0算法的全局最大功率点跟踪(MPPT)控制方法。首先,对电导增量(INC)算法、粒子群(PSO)算法和MFO算法在均匀光照和PSC下分别进行了对比;然后,通过对追踪过程中的飞蛾及火焰位置进行分析,清晰地呈现出了MF0算法的优势;最后,通过仿真和实验证明了MF0算法的有效性。Moth-flame optimization(MFO)algorithm is a novel intelligence algorithm with local optima avoidance and quick convergence,which is also able to provide an efficient solution to the coordination problem between global and local searching.Considering that there would be multiple peaks on the output characteristics curve of photovoltaic(PV)array under partial shading condition(PSC),a control method based on MFO algorithm is presented.First of all,the proposed method is compared with incremental conductance(INC)algorithm and particle swarm optimization(PSO)algorithm under uniform illumination condition and PSC.Then the advantages of MFO algorithm is clearly presented by analyzing the positions of moths and flames in the tracking process.Finally,MFO algorithm is verified under complex experimental condition.Simulation and experimental results indicate that MFO algorithm can quickly and precisely track the global maximum power point under various conditions.

关 键 词：光伏阵列 最大功率点追踪 飞蛾扑火优化算法 局部阴影 

分 类 号：TM615[电气工程—电力系统及自动化]