基于分组粒子群的光伏最大功率点跟踪方法

Maximum Power Point Tracking Method Based on Grouped Particle Swarm Optimization for Photovoltaic Systems

摘要:

针对光伏发电系统在复杂遮阴条件下，光伏输出P-V特性曲线呈现高度非线性，采用基于分组粒子群算法（particle swarm optimization, PSO）和优化的扰动观察法(perturb and observe, P&O)相结合的MPPT（maximum power point tracking）算法进行光伏发电系统输出功率的提升。提出的最大功率点算法分为两个阶段，首先通过将混合蛙跳算法(shuffled frog leaping algorithm, SFLA)的分组思想引入到传统粒子群算法，并采用改进后算法实现近似全局最大功率点的快速搜索，以加快最大功率点跟踪的收敛速度和稳定性。然后，采用优化的扰动观察法实现最大功率点附近的动态精确跟踪，同时减少后续最大功率点跟踪过程中的计算量。通过在不同阶段发挥两种MPPT算法的各自优点来提高光伏最大功率点跟踪控制的效率。最后进行光伏系统遮阴条件变化的仿真实验，与传统粒子群算法相比，提出MPPT方法具有较快的跟踪速度和稳定的功率输出。

Abstract:

The power-voltage (P-V) characteristic curve of photovoltaic (PV) system have highly nonlinear and multiple peaks characteristics under partial shading condition. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system based on an grouped particle swarm optimization (PSO) algorithm and improved perturb and observe (P&O) method in order to improve the output power of photovoltaic system. The proposed maximum power point algorithm is divided into two stages. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced into the basic PSO algorithm, ensuring the differences among particles and the searching of global extremum, and to speed up the convergence speed and stability of maximum power point tracking. And then, the variable step P&O method is used to track the global maximum power point (GMPP) accurately with the change of environment, and at the same time reduce the amount of calculation in the subsequent tracking process of the maximum power point. Through the respective advantages of the two MPPT algorithms at different stages improve the efficiency of photovoltaic maximum power point tracking control. Finally, the superiority of the proposed method over the traditional PSO algorithm in terms of tracking speed and steady-state oscillations is highlighted by simulation and experimental results under partial shading condition.