基于改进粒子群算法的光伏系统附加向心属性最大功率跟踪研究

doi:10.3969/j.issn.2097-0706.2022.03.011

摘要/Abstract

摘要：

对鸟类的群集行为进行研究,观察到在初始阶段鸟类的飞行是随机的,但随着观察时间的增长,外围要素表现出向群的核心移动的趋势。将这种特征特性与传统粒子群算法相结合,开发出一种改进型粒子群算法,增强了算法的全局开发和局部探索能力。通过不同光照条件下的光伏阵列对仿真算法进行对比验证,给出了最大功率点计算值和实测值的跟踪曲线。结果证明所提出的算法在部分遮蔽及变化光照下均能快速、稳定地在线寻得全局最大功率点,较好地解决了传统最大功率跟踪技术容易陷入局部最大功率的问题。

关键词: 光伏发电, 改进型粒子群算法, 最大功率点跟踪, 计算机仿真

Abstract:

In the study on the flocking behavior of birds,it is observed that birds fly randomly at the initial stage,but the peripheral ones show the tendency to move to the core of the flock as time goes.Combining this attribute with the traditional particle swarm algorithm,the improved PSO algorithm is of better global exploitation and local exploration capacities.The performances of different algorithms are measured and compared based on the PV array modeling under various illumination conditions.The results demonstrate that the proposed algorithm can find the global maximum power point quickly and stably under partial shading and changing illumination conditions, and solve the problem that traditional maximum power point tracking （MPPT）are prone to falling into local optima.

Key words: photovoltaic power generation, improved PSO algorithm, MPPT, computer simulation

中图分类号:

TK51

李旭炯, 孙林花, 杨郭明. 基于改进粒子群算法的光伏系统附加向心属性最大功率跟踪研究[J]. 综合智慧能源, 2022, 44(3): 70-76.

LI Xujiong, SUN Linhua, YANG Guoming. MPPT for PV systems appended with centripetal attribute based on improved PSO algorithm[J]. Integrated Intelligent Energy, 2022, 44(3): 70-76.

图/表 14

图1

图2

图3

图4

图5

图6

表1

表2

表3

PSO算法参数

参数	ω	c₁	c₂	$Δ$ R₀	F_n
数值	0.4	2	3	10%	5

表3

图7

表4

图8

图9

表5

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PV 布置	阴影模式与功率峰值/W	跟踪方法	功率/ W	收敛时间/s	跟踪效率/%	能量跟踪因子
7s6p	P1	推荐方法	399.60	1.56	99.99	0.967 8
	399.6（GMPP）	PSO	399.60	2.14	99.98	0.914 6
	356.9（LMPP）	P&O	319.80	2.04	80.03	0.796 1
	325.9（LMPP）	FF	399.60	2.58	99.75	0.930 6
	319.8（LMPP）	DPSO	399.60	2.25	98.56	0.932 8
	314.2（LMPP）	ABC	397.90	2.70	99.58	0.919 1
	247.8（LMPP）	INC	314.20	0.36	78.62	0.765 6
3s6p	P2	推荐方法	32.71	1.82	99.98	0.968 0
	32.7（GMPP）	PSO	32.71	3.05	99.98	0.908 6
	20.4（LMPP）	P&O	32.71	2.02	99.98	0.942 6
		FF	32.71	2.18	99.78	0.910 6
		DPSO	32.71	2.25	99.78	0.941 1
		ABC	32.71	2.10	99.56	0.913 9
		INC	32.71	0.38	99.78	0.957 2

项目	数值
最大功率（P_max）/W	20
开路电压（V_oc）/V	21.0
短路电流（I_sc）/A	1.28
最大功率电压（V_mp）/V	17.2
最大功率电流（I_mp）/A	1.20

试验装置	规格
微控制器	PIC16F876A
PV模块	Innova solar IN-15P
IGBT模块	VID-04
电容/mF	470.0
电感/mH	1.5
电感内阻/Ω	0.358
负载电阻/Ω	100.000

PV 布置	阴影模式与功率峰值/W	跟踪方法	功率/ W	收敛时间/s	跟踪效率/%	能量跟踪因子
4s4p	P3	推荐方法	56.25	1.90	99.98	0.958 3
	56.25（GMPP）	PSO	56.25	3.80	99.98	0.902 8
	47.50（LMPP）	P&O	47.50	0.57	84.44	0.837 8
	P4	推荐方法	47.50	2.54	99.98	0.944 9
	47.50（GMPP）	PSO	47.50	2.90	98.56	0.924 6
	27.50（LMPP）	P&O	47.50	0.10	100.00	0.987 8
3s6p	P5	推荐方法	33.75	1.80	99.99	0.958 5
	33.75（GMPP）	PSO	33.75	2.62	99.98	0.908 2
	23.75（LMPP）	P&O	32.71	1.52	100.00	0.875 7
	P6	推荐方法	48.75	1.70	99.99	0.956 2
	48.75（GMPP）	PSO	48.75	4.00	99.99	0.897 3
	36.85（LMPP）	P&O	36.85	0.10	75.58	0.755 8