基于粒子群优化变步长扰动观察MPPT算法

MPPT Algorithm Based on Particle Swarm Optimizing Variable Step P&O

在复杂的光照条件下,由于光伏阵列被云层局部遮挡,使得其功率-电压(P-V)曲线呈现阶梯状、多极值的形状,从而造成传统的最大功率点跟踪不起作用,陷入局部寻优,影响了光伏阵列的转化效率。针对上述情况,提出了一种可以快速、稳定并且能够全局寻优的最大功率点跟踪(MPPT)算法。算法先将粒子群优化(PSO)改进,使得其能够在一定的迭代次数下稳定地全局更新所有粒子的速度和位置,快速找到最大功率点(MPP)的大概位置,再利用改进的Fibonacci数列作为变步长扰动观察法步长改变的依据,快速接近和得到功率的最佳解。通过Matlab/Simulink建立了仿真模型并且搭建了实验平台,与变步长扰动观察法、传统粒子群优化算法进行比较,验证了算法在精度与速度上均有明显提升。

Under the complex illumination condition , due to the partial covering of photovoltaic array by clouds, the power-voltage （P-V） curve of PV array presents a shape of ladder and multiple extreme, thus resulting that the traditional maximum power tracking doesn＇t work and trapped into local optimization, then the conversion efficiency of PV arrays is affected. In order to solve this problem, the author puts forward a MPPT algorithm with ability of rapid and global optimization. The algorithm uses improved particle swarm optimization algorithm to update the velocity and position under a certain number of iterations, making output power close to the location of maximum power point, and then uses the Fibonacci sequence as the basis of variable step disturbance observation step length change, thus power of the optimal solution is obtained. Simulation model is established through Matlab/Simulink, and experimental plat- form is also set up. Compared with traditional P＆O and particle swarm optimization, the algorithm＇ s precision and speed both are improved.