【摘要】 不断变化的外部环境对光伏列阵的输出有着特殊的影响,为减小能量损失,须对光伏阵列进行最大功率点跟踪(maximum power point tracking,MPPT)。粒子群优化算法(particle swarm optimization,PSO)在多峰值寻优中具有良好的性能,然而粒子在寻优的过程中经常出现过早收敛的现象,导致其寻优精度有所欠缺。为解决以上的缺陷,提出一种改进的自适应粒子群(improved particle swarm optimization,IPSO)与布谷鸟搜索(cuckoo search,CS)混合算法应用于最大功率点跟踪。并在MATLAB/Simulink平台中搭建仿真模型对混合算法进行验证,并与其他方法进行比较,仿真结果证明,改进算法有良好的响应速度和较高的优化精度。更多还原

【Abstract】 The ever-changing external environment has a special impact on the output of photovoltaic arrays. To prevent energy loss, the maximum power point tracking(MPPT) of photovoltaic arrays is particularly important. Particle swarm optimization(PSO) has good performance in multi-peak optimization. However, particles often appear prematurely converging in the process of optimization, and the precision of optimization is also lacking. An improved hybrid particle swarm optimization(IPSO) and cuckoo search(CS) hybrid algorithm for maximum power point tracking were proposed. The simulation was carried out in MATLAB/Simulink and compared with other methods. The results prove that the algorithm has good response speed and high optimization precision.更多还原