基于光伏系统的动态代理模型最大功率点跟踪算法研究

doi:10.3969/j.issn.1674-1951.2021.08.001

摘要/Abstract

摘要：

光伏发电系统在部分阴影条件下,其功率-电压输出特性曲线会出现多个峰值点。针对该问题,传统的最大功率点跟踪算法（MPPT）容易陷入局部最优解且稳定性较差的状况而不再适用。为实现部分阴影条件下最大功率点跟踪,设计了一种基于光伏系统的动态代理模型优化（DSMO）方法。该方法为避免盲目搜索,根据光伏系统的实时数据,采用径向基函数（RBF）网络构建输入/输出特征的动态代理模型。在动态代理模型的基础上,采用贪婪搜索加速收敛。通过恒温恒光照启动测试、恒温光照强度阶跃变化测试、变温变光照强度测试3个算例对该方法的实用性和优越性进行了评估。与蚁群算法（ASO）、灰狼算法（GWO）、扰动观察法（P&O）和粒子群算法（PSO）相比,所提方法在部分阴影条件下能快速稳定地使光伏系统产生更多的能量和更小的功率波动。

关键词: 光伏发电系统, 部分阴影条件, 最大功率点跟踪, 动态代理模型优化, 贪婪搜索

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output characteristic curves of photovoltaic（PV）systems.The traditional maximum power point tracking（MPPT） algorithm is no longer applicable for solving the problem since it is easy to fall into local optimal solution and of poor stability.In order to realize MPPT under PSC,a dynamic surrogate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

中图分类号:

TK01⁺8:TM734

张孝顺, 谭恬, 蒙蝶, 张桂源, 冯永坤. 基于光伏系统的动态代理模型最大功率点跟踪算法研究[J]. 华电技术, 2021, 43(8): 1-10.

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.

图/表 12

图1

图2

表1

PSC下基于DSMO的光伏系统MPPT总流程

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

表1

表2

光伏组件参数

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

表2

表3

DSMO主要参数

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

表3

图3

图4

图5

图6

图7

表4

3种算例下5种算法的功率振荡指标统计结果

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

表4

表5

参考文献 25

[1]	舒杰, 张先勇, 沈玉梁, 等. 可再生能源分布式微网电源规划方法及应用[J]. 控制理论与应用, 2010, 27(5):675-680.
	SHU Jie, ZHANG Xianyong, SHEN Yuliang, et al. The algorithm and application in power sources planning and designing for micro-grid based on distributed renewable energy[J]. Control Theory & Applications, 2010, 27(5):675-680.
[2]	何薇薇, 杨金明, 张淼. 光伏发电最大功率跟踪系统的研究[J]. 控制理论与应用, 2008, 25(1):167-171.
	HE Weiwei, YANG Jinming, ZHANG Miao. Photovoltaic maximum power point tracking system[J]. Control Theory & Applications, 2008, 25(1):167-171.
[3]	周元贵, 陈启卷, 何昌炎, 等. 局部阴影下光伏阵列建模及多峰值MPPT控制[J]. 太阳能学报, 2016, 37(10):2484-2490.
	ZHOU Yuangui, CHEN Qijuan, HE Changyan, et al. Model of PV array under partial shading and MPPT control of multi-peak characteristics[J]. Acta Energiae Solaris Sinica, 2016, 37(10):2484-2490.
[4]	LOKANADHAM M, BHASKAR K V. Incremental conductance based maximum power point tracking(MPPT) for photovoltaic system[J]. International Journal of Engineering Research and Applications(IJERA), 2012, 2(2):1420-1424.
[5]	ALI A I M, SAYED M A, MOHAMED E E M. Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system[J]. International Journal of Electrical Power & Energy Systems, 2018, 99(7):192-202. doi: 10.1016/j.ijepes.2017.12.029
[6]	朱艳伟, 石新春, 但扬清, 等. 粒子群优化算法在光伏阵列多峰最大功率点跟踪中的应用[J]. 中国电机工程学报, 2012, 32(4):42-48.
	ZHU Yanwei, SHI Xinchun, DAN Yangqing, et al. Application of PSO algorithm in global MPPT for PV array[J]. Proceedings of the CSEE, 2012, 32(4):42-48.
[7]	贾林壮, 陈侃, 李国杰, 等. 局部阴影条件下光伏阵列MPPT算法研究[J]. 太阳能学报, 2014, 35(9):1614-1621.
	JIA Linzhuang, CHEN Kan, LI Guojie, et al. The MPPT method research for PV array under shaded condition[J]. Acta Energiae Solaris Sinica, 2014, 35(9):1614-1621.
[8]	SATTAR M A E, ALI H, ELBASET A A, et al. Implementation of a modified perturb and observe maximum power point tracking algorithm for photovoltaic system using an embedded microcontroller[J]. IET Renewable Power Generation, 2016, 10(4):551-560. doi: 10.1049/rpg2.v10.4
[9]	党克, 李鹏举, 刘闯. 基于变步长天牛须搜索算法的光伏系统MPPT控制研究[J]. 吉林电力, 2019, 47(5):27-36.
	DANG Ke, LI Pengju, LIU Chuang. Research on MPPT control of photovoltaic system based on variable-step beetle antennae search algorithm[J]. Jilin Electric Power, 2019, 47(5):27-36.
[10]	梅润杰, 张经炜. 基于Z源逆变器的粒子群和模糊变步长电导增量MPPT算法[J]. 太阳能学报, 2020, 41(1):143-151.
	MEI Runjie, ZHANG Jingwei. MPPT algorithm of particle swarm optimization and fuzzy variable step incremental conductance method based on Z-source inverter[J]. Acta Energiae Solaris Sinica, 2020, 41(1):143-151.
[11]	BABU T S, RAJASEKAR N, SANGEETHA K. Modified particle swarm optimization technique based maximum power point tracking for uniform and under partial shading condition[J]. Applied Soft Computing, 2015, 34(9):613-624. doi: 10.1016/j.asoc.2015.05.029
[12]	MOHANTY S, SUBUDHI B, RAY P K. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J]. IEEE Transactions on Sustainable Energy, 2015, 7(1):1-8.
[13]	JIANG L L, MASKELL D L, PATRA J C. A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions[J]. Energy & Buildings, 2013, 58(2):227-236.
[14]	REZK H, FATHY A, ABDELAZIZ A Y. A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions[J]. Renewable and Sustainable Energy Reviews, 2017, 74(7):377-386. doi: 10.1016/j.rser.2017.02.051
[15]	DARABAN S, PETREUS D, MOREL C. A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading[J]. Energy, 2014, 74(1):374-388. doi: 10.1016/j.energy.2014.07.001
[16]	商立群, 朱伟伟. 基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法[J]. 电工技术学报, 2019, 34(12):2606-2614.
	SHANG Liqun, ZHU Weiwei. Photovoltaic system global maximum power point tracking method based on the global learning adaptive bacteria for aging algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(12):2606-2614.
[17]	ZHOU Z, ONG Y S, NAIR P B, et al. Combining global and local surrogate models to accelerate evolutionary optimization[J]. IEEE Transactions on Systems Man and Cybernetics Part C, 2007, 37(1):66-76. doi: 10.1109/TSMCC.2005.855506
[18]	KANDEMIR E, CETIN N S, BOREKCI S. A comprehensive overview of maximum power extraction methods for PV systems[J]. Renewable and Sustainable Energy Reviews, 2017, 78(10):93-112. doi: 10.1016/j.rser.2017.04.090
[19]	LALILI D, MELLIT A, LOURCI N, et al. State feedback control and variable step size MPPT algorithm of three-level grid-connected photovoltaic inverter[J]. Solar Energy, 2013, 98:561-571. doi: 10.1016/j.solener.2013.10.024
[20]	YANG B, YU T, SHU H C, et al. Perturbation observer based fractional-order PID control of photovoltaics inverters for solar energy harvesting via Yin-Yang-Pair optimization[J]. Energy Conversion and Management, 2018, 171(9):170-187. doi: 10.1016/j.enconman.2018.05.097
[21]	QI J, ZHANG Y B, CHEN Y. Modeling and maximum power point tracking(MPPT) method for PV array under partial shade conditions[J]. Renewable Energy, 2014, 66(6):337-345. doi: 10.1016/j.renene.2013.12.018
[22]	REGIS R G. Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(3):326-347. doi: 10.1109/TEVC.4235
[23]	LIAN J M, LEE Y G, SUDHOFF S D, et al. Self-organizing radial basis function network for real-time approximation of continuous-time dynamical systems[J]. IEEE Transactions on Neural Networks, 2008, 19(3):460-474. doi: 10.1109/TNN.2007.909842
[24]	刘金豆, 成杰, 俞高伟. 基于低压直流配电网并网的并离网一体光储发电系统研究[J]. 华电技术, 2021, 43(4):63-70.
	LIU Jindou, CHENG Jie, YU Gaowei. Research on a PV-energy storage system with integration of grid-connection and disconnection modes based on low-pressure DC distribution network[J]. Huadian Technology, 2021, 43(4):63-70.
[25]	王晗雯, 鲁胜, 周照宇. 光伏-混合储能微电网协调控制及经济性分析[J]. 华电技术, 2020, 42(4):31-36.
	WANG Hanwen, LU Sheng, ZHOU Zhaoyu. Coordinated control and economic analysis on a PV-hybrid energy storage micro-grid system[J]. Huadian Technology, 2020, 42(4):31-36.

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