改进粒子群算法在光伏阵列多峰值MPPT中的应用

张异殊; 王晓文

doi:10.16513/j.cnki.10-1427/tk.2018.01.006

PDF(1294 KB)

分布式能源 ›› 2018, Vol. 3 ›› Issue (1) : 34-38. DOI: 10.16513/j.cnki.10-1427/tk.2018.01.006

改进粒子群算法在光伏阵列多峰值MPPT中的应用

张异殊 ,
王晓文

作者信息 +

Application of Improved Particle Swarm Optimization Algorithm in Multi-Peak MPPT for Photovoltaic Array

Author information +

文章历史 +

摘要

在局部阴影的情况下，光伏阵列的P-U曲线会存在多个峰值点，传统的最大功率跟踪方法在此时会失效。提出基于自适应权重的粒子群算法，基于粒子群算法的全局寻优特性，对局部阴影下的光伏阵列多峰P-U曲线进行寻优，搜寻到最大功率点处对应的电压即为最优输出电压；结合电压闭环调节和Boost电路搭建光伏系统仿真模型，模拟最大功率输出；与传统扰动观察法进行比较并通过Matlab/Simulink进行仿真。

Abstract

In the case of partial shadow, there will be multiple peak points in the P-U curve of the photovoltaic(PV) array, and the conventional maximum power tracking method will fail. This paper proposes a particle swarm optimization algorithm based on adaptive weight. Based on the global optimization characteristics of particle swarm optimization, the multi-peak P-U curve of PV array is optimized under local shadow, and the voltage corresponding to the maximum power point is the best output voltage. Combined with voltage closed-loop regulation and Boost circuit, this paper constructs the photovoltaic system simulation model to simulate the maximum power output, which is compared with traditional perturbation and observation method and simulated in Matlab/Simulink.

导出引用

张异殊, 王晓文. 改进粒子群算法在光伏阵列多峰值MPPT中的应用[J]. 分布式能源. 2018, 3(1): 34-38 https://doi.org/10.16513/j.cnki.10-1427/tk.2018.01.006

Yishu ZHANG, Xiaowen WANG. Application of Improved Particle Swarm Optimization Algorithm in Multi-Peak MPPT for Photovoltaic Array[J]. Distributed Energy Resources. 2018, 3(1): 34-38 https://doi.org/10.16513/j.cnki.10-1427/tk.2018.01.006

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	杨金焕，于化丛，葛亮. 太阳能光伏发电应用技术[M]. 北京：电子工业出版社，2005. 本文引用 [1]

[2]	翟艳烁，马林生，赵全香，等. 太阳能光伏电池的建模与仿真[J]. 电气开关，2012(3): 35-37. ZHAI Yanshuo, MA Linsheng, ZHAO Quanxiang, et al. Modeling and simulation of the potovoltaic battery of solar energy system[J]. Electric Switchgear, 2012(3): 35-37. 本文引用 [1]

[3]	汤济泽，王丛岭，房学法. 一种基于电导增量法的MPPT实现策略[J]. 电力电子技术，2011, 45(4): 73-75. TANG Jize, WANG Conglin, FANG Xuefa. MPPT implementation strategy based on the conductance increment method[J]. Power Electronics, 2011, 45(4): 73-75. 本文引用 [1]

[4]	彭湃，程汉湘，陈杏灿，等. 太阳能光伏电池工程用数学模型的研究[J]. 广东电力，2015, 28(10): 26-29. PENG Pai, CHENG Hanxiang, CHEN Xingcan, et al. Research on engineering mathematic model for solar and photovoltaic cells[J]. Guangdong Electric Power, 2015, 28(10): 26-29. 本文引用 [1]

[5]	HU Xinxin, WANG Lijin, ZHONG Yiwen. An improved particle swarm optimization algorithm for site index curve model[C]//2011 International Conference on Business Management and Electronic Information (BMEI 2011), Guangzhou, China, 2011: 838-842. 本文引用 [2]

[6]	肖景良，徐政，林崇，等. 局部阴影条件下光伏阵列的优化设计[J]. 中国电机工程学报，2009, 29(11): 119-124. XIAO Jingliang, XU Zheng, LIN Chong, et al. Optimal design of photovoltaic arrays under partial shading[J]. Proceedings of the CSEE, 2009, 29(11): 119-124. 本文引用 [1]

[7]	杨佳霖. 基于粒子群算法的分布式能源系统容量优化配置[J]. 分布式能源，2017, 2(6): 46-51. YANG Jialin. Optimization allocation of distributed energy system based on particle swarm optimization algorithm[J]. Distributed Energy, 2017, 2(6): 46-51. 本文引用 [1]

[8]

赵健，周雪松，马幼捷. 基于最优初值电导增量控制的光伏系统最大功率跟踪策略[J]. 电力系统及其自动化学报，2017, 29(4): 20-28.

ZHAO

Jian

, ZHOU

Xuesong

, MA

Youjie

. Maximum power point tracking strategy for photovoltaic system based on optimal initial value of incremental conductance control[J]. Proceedings of the CSU-EPSA, 2017, 29(4): 20-28.

本文引用 [1]

[9]	LU Lin, LUO Qi, LIU Junyong, et al. An improved particle swarm optimization algorithm[C]//IEEE International Conference on Granular Computing, Hangzhou, China, 2008: 486-490. 本文引用 [1]

[10]	程功. 基于预测PI控制的BOOST电路控制算法研究及设计[D]. 上海：东华大学，2017. CHENG Gong. Research an design of boost circuit control algorithm based on predictive PI control[D]. Shanghai: Donghua University, 2017. 本文引用 [1]