基于自适应变异粒子群算法的光伏MPPT控制研究  

RESEARCH ON PHOTOVOLTAIC MPPT CONTROL BASED ON ADAPTIVE MUTATION PARTICLE SWARM OPTIMIZATION ALGORITHM

作　　者：花赟昊 朱武 靳一奇 花明 王世萱 焦哲晶 Hua Yunhao;Zhu Wu;Jin Yiqi;Hua Ming;Wang Shixuan;Jiao Zhejing(College of Electronics and Information Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Binzhou Zhanhua Power Supply Company,State Grid Shandong Electric Power Company,Binzhou 256800,China)

机构地区：[1]上海电力大学电子与信息工程学院,上海200090 [2]山东滨州市沾化区供电公司,滨州256800

出　　处：《太阳能学报》2022年第4期219-225,共7页Acta Energiae Solaris Sinica

基　　金：国家青年基金(61704103);国网上海电力公司科技项目(H2020-073)。

摘　　要：针对局部遮阴(PSC)时,光伏阵列输出P-U曲线出现多峰值现象,传统最大功率点追踪(MPPT)算法易陷入局部最优解,无法追踪到全局最优解的问题,该文提出基于自适应变异粒子群算法的光伏MPPT控制方法。在粒子群算法寻优过程中同步调整学习因子与惯性权重,以提高算法收敛速度与精度,同时引入变异机制以扩大粒子搜索范围,增强算法的全局寻优能力。仿真结果表明,与标准粒子群算法相比,该算法在均匀光照,静态、动态局部遮阴条件下,均能快速准确的追踪到最大功率点,其收敛速度更快,稳态精度更高。For the problem of partial shading condition(PSC),there will be multi-peaks phenomenon in the output P-U curve of photovoltaic array,and the traditional maximum power point tracking(MPPT)algorithm can easily fall into local optimal solution and cannot track down the global optimal solution.Based on the above,a photovoltaic MPPT control method based on adaptive mutation particle swarm optimization is proposed in this paper.The learning factors and inertia weights are adjusted synchronously during particle swarm optimization in order to improve the convergence speed and accuracy of the algorithm.At the same time,the mutation mechanism is introduced to expand the particle search range and enhance the global optimization of the algorithm.Simulation results show that the algorithm can quickly and accurately track the maximum power point under uniform illumination,static and dynamic partial shading conditions,with faster convergence and higher steady-state accuracy than the standard particle swarm optimization.

关 键 词：光伏发电 最大功率点追踪 局部遮阴 变异粒子群算法 

分 类 号：TM615[电气工程—电力系统及自动化]