复杂遮蔽条件下光伏多峰出力特征及GMPPT控制

陈明轩; 武建文; 马速良; 黄炼

doi:10.13700/j.bh.1001-5965.2016.0478

复杂遮蔽条件下光伏多峰出力特征及GMPPT控制

doi: 10.13700/j.bh.1001-5965.2016.0478

北京航空航天大学自动化科学与电气工程学院, 北京 100083

基金项目:

国家自然科学基金 51377007

高等学校博士学科点专项科研基金 20131102130006

详细信息

作者简介:
陈明轩, 男, 博士研究生。主要研究方向:电力电子技术在新能源领域应用、分布式能源系统与控制

武建文, 男, 博士, 教授, 博士生导师。主要研究方向:电力电子技术、智能电器

马速良, 男, 博士研究生。主要研究方向:微电网群控制、电力电子变换器建模及非线性控制、储能技术及控制

黄炼, 男, 硕士研究生。主要研究方向:新能源接入、分布式能源系统与控制、电力电子技术

通讯作者:
武建文, E-mail:wujianwen@vip.sina.com

中图分类号: TM615
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出版历程
- 收稿日期: 2016-06-03
- 录用日期: 2016-09-21
- 网络出版日期: 2017-06-20

Photovoltaic multi-peak output characteristics and GMPPT control under complex shaded condition

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Funds:

National Natural Science Foundation of China 51377007

Specialized Research Fund for the Doctoral Program of Higher Education of China 20131102130006

More Information

Corresponding author: WU Jianwen, E-mail:wujianwen@vip.sina.com

摘要

摘要:
针对光伏发电系统中最大功率点跟踪（MPPT）算法在遮蔽情况下失效问题，提出了一种基于δ势阱的量子粒子群全局MPPT（GMPPT）算法。结合光照强度变化时的光伏多峰值出力特征，从光伏最大功率点变迁角度出发，分析常规MPPT算法存在搜索盲区的原因，说明GMPPT寻优必要性。提出一种提高粒子多样性、搜索速度及收敛精度的量子行为粒子群优化（QPSO）算法。在MATLAB/SIMSCAPE平台下，结合算例分析，对比标准粒子群优化（PSO）算法，验证所提优化算法在有效GMPPT的情况下，具有参数少、搜索快的特点，同时全局搜索能力强，防早熟效果明显，适用于GMPPT的实现。
- 光伏发电 /
- 光伏阵列 /
- 局部阴影 /
- 全局最大功率点跟踪(GMPPT) /
- 量子行为粒子群优化(QPSO)算法
Abstract:
Aimed at solving the failure problem of the maximum power point tracking (MPPT) algorithm caused by partially shaded condition in the photovoltaic power generation system, a global maximum power point tracking (GMPPT) algorithm based on δ-potential well is proposed. Based on the photovoltaic multi-peak output characteristics when the illumination intensity is changing, the reason of searching blind spot in conventional MPPT algorithm is analyzed in terms of maximum power point transition, and the necessity of GMPPT optimization is explained. A quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to improve the particle diversity and increase the search speed and convergence accuracy. The algorithm was verified by MATLAB/SIMSCAPE and compared with the standard particle swarm optimization (PSO) algorithm. The results show that the proposed algorithm can track the global maximum power point effectively with fast searching speed, reducing the dependency on parameters and avoiding premature convergence of the algorithm.
- photovoltaic power generation /
- photovoltaic array /
- partially shaded /
- global maximum power point tracking (GMPPT) /
- quantum-behaved particle swarm optimization (QPSO) algorithm

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图 1 光伏阵列结构

Figure 1. Structure of photovoltaic array

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图 2 Boost电路原理

Figure 2. Principle of Boost circuit

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图 3 不同光照条件下，光伏输出U-I和U-P曲线

Figure 3. Photovoltaic output U-I and U-P curves under different illumination conditions

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图 4 基于δ势阱的QPSO算法流程图

Figure 4. Flowchart of QPSO algorithm based on δ-potential well

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图 5 标准PSO和QPSO算法在遮蔽1下的搜索轨迹对比

Figure 5. Comparison of search trajectories between standard PSO and QPSO algorithms under shaded 1

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图 6 QPSO和标准PSO算法在遮蔽1下的搜索结果对比

Figure 6. Comparison of search results between QPSO and standard PSO algorithms under shaded 1

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图 7 QPSO和标准PSO算法在遮蔽1下的全局最优值曲线对比

Figure 7. Comparison of global optimum curves between QPSO and standard PSO algorithms under shaded 1

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图 8 QPSO和标准PSO算法GMPPT效果图

Figure 8. Effect pictures of GMPPT by QPSO and standard PSO algorithms

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表 1 不同工况下光照强度

Table 1. Illumination intensity under different conditions

光照模式	光照强度/(W·m^-2)
光照模式	标准光照	遮蔽1	遮蔽2
G₁₁	1 000	1 000	1 000
G₁₂	1 000	1 000	1 000
G₂₁	1 000	500	800
G₂₂	1 000	300	500
G₃₁	1 000	200	200
G₃₂	1 000	200	200

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表 2 单块光伏电池板参数

Table 2. Parameters of single photovoltaic cell panel

参数	数值
U_oc/V	37.67
I_sc/A	8.81
P_m/W	254.9
U_m/V	31.8
I_m/A	8.18

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表 3 Boost电路及算法参数

Table 3. Boost circuit and algorithm parameters

参数	数值
负载电阻R/Ω	200
粒子数N	5
迭代次数G_max	20
收缩-扩张系数β	1.2
自变量范围[D_min, D_max]	[0, 1]

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表 4 收敛准确率σ对照

Table 4. Comparison of convergence accuracy rate σ

算法	σ/%
算法	标准光照	遮蔽1	遮蔽2
QPSO	100	95	100
标准PSO	100	80	90

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