基于光伏组件参数辨识的故障诊断分析

doi:10.16183/j.cnki.jsjtu.2023.503

摘要/Abstract

摘要：

为利用光伏板运行数据辨识光伏电池的重要参数,并通过这些参数诊断其运行状态,将二阶Bézier函数与自适应战争策略算法相结合,得到硅基光伏电池单二极管拓扑中光生电流、二极管反向饱和电流、二极管理想因子、串联电阻和并联电阻5个未知参数最优解的方法;对阴影、老化、短路和开路4种常见的故障进行理论和仿真分析.通过实验验证,对比辨识结果中参数的变化与故障类型,得出光伏单二极管模型5个参数与4种典型故障类型存在一定的对应关系,并得到不同故障类型下光伏组件各输出量的变化规律,为光伏电池故障判别及电池性能的判断提供参考.

关键词: 光伏电池, 单二极管模型, 参数辨识, 故障判别, Bézier函数, 战争策略优化

Abstract:

To utilize the operation data of photovoltaic(PV) panels for identifying key parameters of PV cells and diagnosing their operation status, the second-order Bézier function is combined with the adaptive war strategy optimization algorithm to obtain the optimal solutions for the five unknown parameters of silicon-based PV cell single diode topology: photogenerated current, diode reverse saturation current, diode ideal factor, series resistance and parallel resistance. The method includes theoretical and simulation analyses of four common faults, namely, shadowing, aging, short-circuit and open-circuit. By comparing the changes of the parameters in the identification results and the types of faults based on experimental validation, it is concluded that there is a certain correspondence between the five parameters of the PV single diode model and the four typical fault types. Additionly, the output behavior of PV modules in different fault types is characterized, which provides a reference for the identification of PV cell faults and the judgment of battery performance.

Key words: photovoltaic (PV) cell, single diode model, parameter identification, fault discrimination, Bézier function, war strategy optimization

中图分类号:

TM615
TP181

吕艳玲, 钟晨, 刘志鹏. 基于光伏组件参数辨识的故障诊断分析[J]. 上海交通大学学报, 2025, 59(9): 1383-1396.

LÜ Yanling, ZHONG Chen, LIU Zhipeng. Fault Diagnosis Analysis Based on Parameter Identification of Photovoltaic Module[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1383-1396.

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参考文献 25

[1]	夏超浩. 光伏组件模型参数辨识及其故障识别研究[D]. 西安: 西安理工大学, 2021.
	XIA Chaohao. Research on parameter identification and fault identification of photovoltaic module model[D]. Xi’an: Xi’an University of Technology, 2021.
[2]	GAO X K, CUI Y, HU J J, et al. Parameter extraction of solar cell models using improved shuffled complex evolution algorithm[J]. Energy Conversion & Management, 2018, 157: 460-479.
[3]	CHIN V J, SALAM Z, ISHAQUE K. An accurate and fast computational algorithm for the two-diode model of PV module based on a hybrid method[J]. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6212-6222.
[4]	师楠, 朱显辉, 李一丹, 等. 不同迭代算法求解光伏模块参数的收敛速度[J]. 电力科学与技术学报, 2020, 35(3): 55-60.
	SHI Nan, ZHU Xianhui, LI Yidan, et al. Study on theconvergence rate of different iterative algorithms in solving PV model parameters[J]. Journal of Electric Power Science & Technology, 2020, 35(3): 55-60.
[5]	LIANG J, GE S L, QU B Y, et al. Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models[J]. Energy Conversion & Management, 2020, 203: 112138.
[6]	朱显辉, 钟敬文, 师楠, 等. 基于改进自适应粒子群算法的光伏电池参数识别[J]. 黑龙江科技大学学报, 2022, 32(6): 784-789.
	ZHU Xianhui, ZHONG Jingwen, SHI Nan, et al. Parameters identification of photovoltaic cells based on an improved adaptive particle swarm algorithm[J]. Journal of Heilongjiang University of Science & Technology, 2022, 32(6): 784-789.
[7]	刘斌, 谈竹奎, 唐赛秋, 等. 基于数据预测启发式算法的光伏电池参数识别[J]. 电力系统保护与控制, 2021, 49(23): 72-79.
	LIU Bin, TAN Zhukui, TANG Saiqiu, et al. Photovoltaic cell parameter extraction using data prediction based on a meta-heuristic algorithm[J]. Power System Protection & Control, 2021, 49(23): 72-79.
[8]	KHURSHEED M U N, ALGHAMDI M A, NADEEM KHAN M F, et al. PV model parameter estimation using modified FPA with dynamic switch probability and step size function[J]. IEEE Access, 2021, 9: 42027-42044.
[9]	闫真, 李水佳, 龚文引. 基于分解的自适应差分进化识别光伏模型参数[J]. 应用科学学报, 2022, 40(5): 713-726.
	YAN Zhen, LI Shuijia, GONG Wenyin. Parameter identification of photovoltaic models based on adaptive differential evolution with decomposition[J]. Journal of Applied Sciences, 2022, 40(5): 713-726.
[10]	CHEN X, XU B, MEI C L, et al. Teaching-learning-based artificial bee colony for solar photovoltaic parameter estimation[J]. Applied Energy, 2018, 212: 1578-1588.
[11]	韩伟, 王宏华, 陈凌, 等. 光伏组件参数拟合及输出特性研究[J]. 电力自动化设备, 2015, 35(9): 100-107.
	HAN Wei, WANG Honghua, CHEN Ling, et al. Research on parameter fitting and output characteristics of PV module[J]. Electric Power Automation Equipment, 2015, 35(9): 100-107.
[12]	乔苏朋, 杨艳, 陈世群, 等. 光伏阵列故障检测方法综述[J]. 电气技术, 2021, 22(7): 1-6.
	QIAO Supeng, YANG Yan, CHEN Shiqun, et al. Review on photovoltaic array diagnosis methods[J]. Electrical Engineering, 2021, 22(7): 1-6.
[13]	彭雅兰, 李志刚. 太阳能光伏阵列在线故障诊断方法综述[J]. 电器与能效管理技术, 2019(11): 1-7.
	PENG Yalan, LI Zhigang. A review on online fault diagnosis method for PV arrays[J]. Electrical & Energy Management Technology, 2019(11): 1-7.
[14]	余玲珍, 杨靖, 龙道银, 等. 典型故障条件下光伏阵列建模与仿真[J]. 电网与清洁能源, 2020, 36(10): 103-111.
	YU Lingzhen, YANG Jing, LONG Daoyin, et al. Modeling and simulation of photovoltaic arrays under typical fault conditions[J]. Power System & Clean Energy, 2020, 36(10): 103-111.
[15]	TREJOS-GRISALES L A, BASTIDAS-RODRÍGUEZ J D, RAMOS-PAJA C A. Mathematical model for regular and irregular PV arrays with improved calculation speed[J]. Sustainability, 2020, 12(24): 10684.
[16]	宋文海, 李田泽, 田晖, 等. 基于概率神经网络的光伏阵列故障诊断研究[J]. 电源技术, 2019, 43(9): 1525-1527.
	SONG Wenhai, LI Tianze, TIAN Hui, et al. Research on fault diagnosis of PV array based on PNN[J]. Chinese Journal of Power Sources, 2019, 43(9): 1525-1527.
[17]	吴擎. 基于深度学习的光伏发电系统故障诊断方法研究[D]. 济南: 山东大学, 2019: 34-40.
	WU Qing. Research on fault diagnosis methods of photovoltaic power systems based on deep learning. Jinan: Shandong University, 2019: 34-40.
[18]	CHEN Z C, WU L J, CHENG S Y, et al. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics[J]. Applied Energy, 2017, 204: 912-931.
[19]	LU X Y, LIN P J, CHENG S Y, et al. Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph[J]. Energy Conversion & Management, 2019, 196: 950-965.
[20]	AYYARAO T S L V, RAMAKRISHNA N S S, ELAVARASAN R M, et al. War strategy optimization algorithm: A new effective metaheuristic algorithm for global optimization[J]. IEEE Access, 2022, 10: 25073-25105.
[21]	许建伟, 崔东文. 战争策略算法与变色龙算法优化极限学习机的输沙量时间序列预测[J]. 水力发电, 2022, 48(11): 36-42.
	XU Jianwei, CUI Dongwen. Time series prediction of sediment discharge by optimizing extreme learning machine with war strategy and chameleon swarm algorithm[J]. Water Power, 2022, 48(11): 36-42.
[22]	ELHOSENY M, THARWAT A, HASSANIEN A E. Bezier curve based path planning in a dynamic field using modified genetic algorithm[J]. Journal of Computational Science, 2018, 25: 339-350.
[23]	师楠, 徐明忠, 朱显辉, 等. 多晶硅I-V曲线的Bezier控制点拟合规律[J]. 中国电机工程学报, 2022, 42(6): 2200-2208.
	SHI Nan, XU Mingzhong, ZHU Xianhui, et al. Bezier control point fitting rule of polysilicon I-V curve[J]. Proceedings of the CSEE, 2022, 42(6): 2200-2208.
[24]	RAMESH T, RAJANI K, PANDA A K. A novel triple-tied-cross-linked PV array configuration with reduced number of cross-ties to extract maximum power under partial shading conditions[J]. CSEE Journal of Power & Energy Systems, 2021, 7(3): 567-581.
[25]	高献坤. 光伏电池组件等效电路模型参数提取研究[D]. 郑州: 河南农业大学, 2015.
	GAO Xiankun. Parameter extraction of solar cell equivalent circuit model. Zhengzhou: Henan Agricultural University, 2015.

算法	I_ph/A	I_o/A	a	R_s/Ω	R_sh/Ω	ε_RMSE
PSO	7.5162	9.1017×10^-5	1.7952	0.00100	15597.17766	0.0405
WSO	7.4667	3.8753×10^-6	1.4325	0.15062	5143.03559	0.0165
AWSO	7.4495	1.7488×10^-6	1.3617	0.17540	13117.78833	0.0139

算法	ε_RMSE
算法	最大值	最小值	平均值
PSO	0.07479	0.04057	0.05412
WSO	0.02476	0.01635	0.01989
AWSO	0.01393	0.01304	0.01351

工况1		工况2		工况3
V/V	I/A	V/V	I/A	V/V	I/A
23.30	0	23.022	0	22.851	0
23.35	0.0630	23.160	0.0010	22.610	0.1650
23.10	0.1729	22.910	0.1190	22.320	0.2779
22.87	0.2619	22.600	0.2440	22.270	0.2980
22.81	0.2899	22.560	0.2599	22.160	0.3359
22.70	0.3269	22.420	0.3089	22.040	0.3769
22.56	0.3750	22.310	0.3450	21.820	0.4569
22.48	0.3989	22.190	0.3848	21.570	0.5139
22.39	0.4269	21.870	0.4900	21.470	0.5400
22.06	0.5190	21.640	0.5380	21.270	0.5880
22.05	0.5239	21.330	0.6029	21.060	0.6320
21.97	0.5440	21.260	0.6170	20.920	0.6630
21.79	0.5829	21.240	0.6210	20.630	0.7090
21.57	0.6289	21.110	0.6449	20.420	0.7420
21.37	0.6679	20.760	0.6989	20.030	0.7869
21.02	0.7210	20.560	0.7279	19.720	0.8199
20.87	0.7440	20.230	0.7649	18.410	0.8979
20.49	0.7850	19.660	0.8170	18.400	0.8989
19.96	0.8309	18.910	0.8619	17.300	0.9300
0	0.9480	0	0.9340	0	0.9639

工况	I_ph/A	I_o×10⁸/A	a	R_s/Ω	R_sh/mΩ	ε_RMSE
1	0.9481	1.44	1.6404	0.1796	6.34	0.0045
2	0.9341	6.98	1.6406	0.4309	2.55	0.0044
3	0.9673	4.11	1.6842	0.3583	2.68	0.0052

工况	V/V	I/A	工况	V/V	I/A
工况4	22.301	0	工况5	22.286	0
	22.360	0.0010		22.700	0.0740
	22.360	0.0010		22.530	0.1480
	22.360	0.0010		22.300	0.2399
	22.360	0.0010		22.210	0.2710
	22.360	0.0010		22.110	0.3050
	22.360	0.0010		21.970	0.3500
	22.360	0.0020		21.890	0.3849
	22.360	0.0020		21.510	0.4829
	22.360	0.0020		21.420	0.5039
	22.300	0.0170		21.140	0.5650
	21.970	0.0670		21.040	0.5860
	22.000	0.0630		20.840	0.6230
	21.990	0.0650		20.260	0.6919
	21.730	0.0780		15.090	0.7099
	0	0.0930		0	0.7869
工况6	2.333	0	工况7	22.118	0
	16.630	0.1070		22.650	0.0020
	11.420	0.1299		22.500	0.0659
	8.701	0.1529		22.330	0.1350
	0.400	0.1619		22.230	0.1679
	0.400	0.1610		22.060	0.2279
	0.398	0.1560		22.040	0.2349
	0.395	0.1519		21.940	0.2619
	0.395	0.1480		21.850	0.2910
	0.394	0.1449		21.760	0.3140
	0.391	0.1360		21.620	0.3470
	0.389	0.1289		11.550	0.4659
	0.388	0.1240		5.217	0.4840
	0.386	0.1210		5.119	0.4829
	0.384	0.1220		0.922	0.5079
	0	0.1740		0	0.5170