Study on dynamic surrogate model for MPPT of PV systems

doi:10.3969/j.issn.1674-1951.2021.08.001

Abstract

Abstract:

Under partial shading condition（PSC）,there will be multiple peaks on the power-voltage output characteristic curves of photovoltaic（PV）systems.The traditional maximum power point tracking（MPPT） algorithm is no longer applicable for solving the problem since it is easy to fall into local optimal solution and of poor stability.In order to realize MPPT under PSC,a dynamic surrogate model-based optimization（DSMO） method for a PV system is designed.To avoid aimless search,by taking real-time data of the PV system,the radial basis function（RBF） network is adopted to construct the dynamic surrogate model of input/output features.Based on the dynamic surrogate model,greedy search is used to accelerate the convergence.The practicability and superiority of the method are evaluated by tests under three conditions,constant temperature and constant light start-up experiment,constant temperature and step-changed light test,and variable temperature and variable light test.Compared with ant colony algorithm（ASO）,gray wolf optimizer（GWO）,perturb and observation method（P&O） and particle swarm optimization algorithm（PSO）,the DSMO method proposed can facilitate PV systems to generate more energy and smaller power fluctuation quickly and stably under PSC.

Key words: PV system, partial shading condition, MPPT, DSMO, greedy search

CLC Number:

TK01⁺8:TM734

ZHANG Xiaoshun, TAN Tian, MENG Die, ZHANG Guiyuan, FENG Yongkun. Study on dynamic surrogate model for MPPT of PV systems[J]. Huadian Technology, 2021, 43(8): 1-10.

Figures/Tables 12

Fig.1

Fig.2

Tab.1

Overall execution procedure of DSMO for MPPT of the PV system under PSC

步骤	内容
1	初始化算法参数和训练样本（式（9））
2	设置 $k = 1$
3	$FOR$ $i = 1$ to $n$
4	将第 $i$ 个训练样本的占空比信号输入到PWM中
5	采集光伏发电系统的实时电压、电流信号
6	计算第 $i$ 个训练样本的期望输出功率值（式（10））
7	END FOR
8	WHILE $k$ ≤ $k max$
9	通过训练RBF神经网络构建代理模型
10	更新搜索范围的上、下限（式（8））
11	确定离散搜索点的最小数量（式（11））
12	执行贪婪搜索过程（式（5—7））
13	将获得的新的占空比信号输入到PWM中
14	采集光伏发电系统的实时电压、电流信号
15	计算当前占空比对应的期望输出功率值（式（10））
16	往代理模型中增加新的训练样本
17	设置 $k = k + 1$
18	END WHILE
19	输出最优的占空比信号
20	当光照强度发生变化时,重复步骤1—19

Tab.1

Tab.2

PV system parameters

参数	值	参数	值
峰值功率/W	249	额定工作温度 ( $T ref$ )/K	298.15
峰值功率下的电压/V	30.000 0	二极管理想因子( $A$ )	0.948 12
峰值功率下的电流/A	8.300 0	光生电流( $I g$ )/A	8.838 2
短路电流( $I sc$ )/A	8.830 0	二极管反向饱和电流( $I s$ )/A	$1.013 2 e - 10$
开路电压( $V oc$ )/V	36.8	并联电阻( $R sh$ )/ $Ω$	314.760 0
$I sc$ 的温度系数( $C 1$ )/（mA·℃^-1）	6.38	串联电阻( $R s$ )/ $Ω$	0.291 4

Tab.2

Tab.3

Main parameters of DSMO

参数	$n 0$	$δ$	$k max$	$σ$
值	10	0.000 1	10	1

Tab.3

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Tab.4

Power oscillation indexes in three cases calculated by five methods

算例	指标	ASO	GWO	P&O	PSO	DSMO
恒温恒光照启动测试	能量/( $kW · s$ )	1.006 0	0.999 3	0.735 7	0.903 1	1.139 6
	$Δ P max / ‰$	0.122 1	0.145 2	0.106 5	0.165 6	0.109 2
	$Δ P avg / ‰$	0.012 1	0.011 3	0.001 8	0.012 7	0.011 4
恒温光照强度阶跃变化测试	能量/( $kW · s$ )	4.276 4	4.388 2	3.433 4	4.432 5	4.671 3
	$Δ P max / ‰$	0.143 6	0.160 7	0.114 1	0.167 3	0.133 2
	$Δ P avg / ‰$	0.014 2	0.021 9	0.001 0	0.013 7	0.013 7
变温变光照强度测试	能量/( $kW · s$ )	5.365 8	5.177 9	3.690 3	5.191 1	5.578 4
	$Δ P max / %$	0.129 5	0.141 5	0.106 1	0.161 8	0.111 6
	$Δ P avg / %$	0.014 0	0.026 4	0.009 1	0.013 9	0.010 5

Tab.4

Tab.5

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算例	ASO	GWO	PSO	DSMO
恒温恒光照启动测试	1.534	1.514	1.584	1.045
恒温光照强度阶跃变化测试	1.637/2.062/4.085/ 6.103/8.126	0.991/2.074/4.102/ 6.095/8.135	1.635/2.067/4.095/ 6.108/8.138	0.102/2.049/4.064/ 6.089/8.119
变温变光照强度测试	1.154/3.083/5.045/ 6.023/9.035	1.127/3.072/5.048/ 6.019/9.034	1.591/3.075/5.058/ 6.025/9.042	1.021/3.058/5.033/ 6.016/9.035