结合量子粒子群算法的光伏多峰最大功率点跟踪改进方法

光伏阵列在局部阴影时的P-U曲线呈现多峰特性,需要设计光伏多峰最大功率点跟踪方法,以实现光伏发电最大功率输出,提高光伏发电效率。相比粒子群优化算法,量子粒子群优化算法具有收敛速度更快和全局收敛性等优势。提出了一种基于量子粒子群优化算法的光伏多峰最大功率点跟踪改进方法。该方法采用量子粒子群优化算法实现最大功率点的全局搜索;根据光伏阵列在局部阴影时P-U曲线上功率极值点的分布特点初始化种群中的粒子总数及其电压;并根据量子粒子群优化算法收敛时粒子自身最优位置的特点,提出了更适合光伏多峰最大功率点跟踪的收敛判据。仿真测试表明,提出的改进方法能够快速有效地实现光伏多峰最大功率点跟踪,收敛速度更快,避免了不收敛的问题,且具有应对光照情况变化的能力,提高了局部阴影时光伏发电的效率。

Improved Maximum Power Point Tracking Method for Photovoltaic Multi-peak Based on Quantum-behaved Particle Swarm Optimization Algorithm

The P-U curves of partially shaded photovoltaic arrays are of multi-peak, which makes it necessary to design a method for photovoltaic multi-peak maximum power point tracking(MPPT)to realize the maximum power output and to improve the efficiency of photovoltaic power generation. Compared with the particle swarm optimization(PSO)algorithm, the quantum-behaved particle swarm optimization(QPSO)algorithm has advantages such as a faster convergence rate and global convergence. An improved method for photovoltaic multi-peak MPPT based on the QPSO algorithm is proposed. The method applies the QPSO algorithm to the global search for the maximum power point. The total number and the voltages of the particles are initialized according to the distribution character of the extreme points on the P-U curves of the partially shaded photovoltaic arrays. A convergence criterion which is more suitable for photovoltaic multi-peak MPPT is proposed according to the characteristics of the particles'' personal best voltages when the QPSO algorithm converges. The simulation test shows that the improved method can realize multi-peak MPPT quickly and effectively, and avoids the non-convergence problem. It also has the ability to respond to the change of the light condition, and improves the efficiency of the photovoltaic power generation when partially shaded.