【摘要】 局部阴影下，在光伏最大功率点追踪（maximum power point tracking,MPPT）过程中，以往的方法通常采用的是全局搜索，这一过程会出现搜索时间长、收敛速度慢、局部寻优、功率振荡时间长等问题。对此，提出双优化粒子群算法（particle swarm optimization,PSO），即先通过数学推导优化最大功率点存在的电压区间，由全局缩小范围，在此区间内再运用PSO搜索，并优化算法中的系数c₁、c₂，引入搜索程度因子d来增强其收敛性。在MATLAB/Simulink仿真中设置两组光照并通过全局搜索算法中的传统PSO与改进PSO进行对比分析。仿真显示该方法在上述问题方面均有较为明显的改善。更多还原

【Abstract】 Existing methods generally perform the process of photovoltaic maximum power point tracking（MPPT） in the case of shadow formed by local structures by adopting global search, which leads to increased search duration,slow convergence, local optimization, long power oscillation time period, and other problems. In this paper, a double optimization particle swarm optimization（PSO） algorithm is proposed to optimize the global search. First, the voltage range of the maximum power point is optimized by mathematical derivation to reduce the global range. Then, PSO search is applied in this range. For the coefficients c₁and c₂ in the algorithm, this paper introduces the search degree factor δ to enhance the convergence. Two illumination sets were set up in a MATLAB/Simulink simulation for performance comparison of the traditional and improved PSO algorithms when conducting a global search.Simulation results show that our proposed method has a noticeable improvement in the above problems.更多还原