电力系统自动化2016，Vol.40Issue(23)：101-108,8.DOI:10.7500/AEPS20160304010
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
Improved Maximum Power Point Tracking Method for Photovoltaic Multi-peak Based on Quantum-behaved Particle Swarm Optimization Algorithm
韩鹏 1李银红 1何璇 1付元欢 1游昊 2李本瑜2
作者信息
- 1. 强电磁工程与新技术国家重点实验室 华中科技大学，湖北省武汉市 430074
- 2. 云南电力调度控制中心，云南省昆明市 650011
- 折叠
摘要
Abstract
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.关键词
光伏发电/最大功率点跟踪/粒子群优化算法/量子粒子群优化算法Key words
photovoltaic power generation/maximum power point tracking (MPPT)/particle swarm optimization (PSO) algorithm/quantum-behaved particle swarm optimization (QPSO) algorithm引用本文复制引用
韩鹏,李银红,何璇,付元欢,游昊,李本瑜..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[J].电力系统自动化,2016,40(23):101-108,8.
