Modeling of Efficiency Distribution Characteristics of Photovoltaic Arrays and Its Application in Operating State Evaluation

doi:10.12096/j.2096-4528.pgt.23067

Abstract

Abstract:

Objectives As the installed capacity of photovoltaic (PV) power generation continues to expand, accurately evaluating the operating state of PV arrays has become increasingly important in the intelligent operation of PV power stations. Due to the influence of various uncontrollable factors such as clouds, weather, and environmental conditions, the output of PV arrays shows significant uncertainty, posing challenges to the analysis of their operating state. To address this issue, a modeling method for efficiency distribution characteristics of PV arrays based on nonparametric estimation is proposed, and the modeling results are used to evaluate the operating state of PV arrays. Methods First, the distribution characteristics and influencing factors of PV array efficiency are analyzed. Next, a distribution model of PV array efficiency is established using the kernel density estimation method. Finally, by setting the confidence levels, threshold intervals for different operating states of PV arrays are determined, enabling the identification of the operating state of PV arrays. Results Using actual operating data, the proposed method is validated across multiple dimensions, including output current, voltage, and efficiency of PV arrays, demonstrating its effectiveness in classifying the operating state of PV arrays. Conclusions The efficiency distribution characteristics of PV arrays can effectively represent the operating state, addressing the uncertainty in state classification caused by random fluctuations in PV output and enabling precise identification of PV array operating states.

Key words: photovoltaic arrays, photovoltaic power stations, state evaluation, power generation efficiency, nonparametric estimation, kernel density estimation, intelligent operation

CLC Number:

TK 01

Xiaofei LI, Zhao WANG, Yinnan LIU, An WU, Guanghui LI, Liusheng PEI, Haifeng WANG, Jiaqi LI, Honglu ZHU. Modeling of Efficiency Distribution Characteristics of Photovoltaic Arrays and Its Application in Operating State Evaluation[J]. Power Generation Technology, 2025, 46(1): 83-92.

Figures/Tables 11

Tab. 1 Distribution of efficiency and its correlation with various factors

参数	影响因素
参数	辐照度	风速	湿度	温度	电压	电流	功率
最大值	0.829	0.384	0.017	0.604	0.920	0.867	0.873
最小值	-0.031	-0.013	-0.458	-0.042	-0.027	-0.032	-0.032
平均值	0.547	0.322	-0.305	0.397	0.812	0.566	0.573

Fig. 1 Efficiency distribution of photovoltaic arrays

Fig. 2 Kernel density distribution curve of daily efficiency for arrays in the entire station

Fig. 3 Probability density distribution of efficiency for different arrays

Fig. 4 Flowchart of operating state classification algorithm

Fig. 5 Operating state evaluation of arrays in the entire station

Fig. 6 Efficiency comparison of arrays in different states

Fig. 7 Power comparison of arrays in different states

Fig. 8 Voltage comparison of arrays in different states

Fig. 9 Current comparison of arrays in different states

Fig. 10 Comparison of fitted efficiency distribution curves of arrays in different states

References 18

1	国家能源局．2022年光伏发电建设运行情况[EB/OL]．(2023-02-17)[2023-03-15]．．
	National Energy administration ．Photovoltaic power generation construction and operation in 2022[EB/OL]．(2023-02-17)[2023-03-15]．．
2	李翰章，冯江涛，王鹏程，等．基于TDE-SO-AWM-GRU的光伏发电功率预测模型[J]．中国电力，2024，57(12)：41-49．
	LI H Z， FENG J T， WANG P C，et al ．Photovoltaic power prediction model based on TDE-SO-AWM-GRU[J]．Electric Power，2024，57(12)：41-49．
3	罗耿．山地光伏阵列布置方法和排间距计算[J]．发电技术，2022，43(2)：320-327． doi:10.12096/j.2096-4528.pgt.20005
	LUO G ．Layout method and row pitch calculation for mountain photovoltaic array[J]．Power Generation Technology，2022，43(2)：320-327． doi:10.12096/j.2096-4528.pgt.20005
4	韩晓，王涛，韦晓广，等．考虑阵列间时空相关性的超短期光伏出力预测[J]．电力系统保护与控制，2024，52(14)：82-94．
	HAN X， WANG T， WEI X G，et al ．Ultrashort-term photovoltaic output forecasting considering spatiotemporal correlation between arrays[J]．Power System Protection and Control，2024，52(14)：82-94．
5	李雪，刘全，余荣祖，等．并网光伏电站生产运行数据敏捷分析方法研究[J]．电网与清洁能源，2014，30(3)：104-109．
	LI X， LIU Q， YU R Z，et al ．Study on the agile analysis method for the production operation data of the grid-connected PV power station[J]．Power System and Clean Energy，2014，30(3)：104-109．
6	林成楷，程远达，杨晋明，等．应用灰色关联改进TOPSIS法的双层光伏外窗建筑采光与能耗性能评价[J]．华侨大学学报(自然科学版)，2021，42(6)：792-799．
	LIN C K， CHENG Y D， YANG J M，et al ．Daylighting and energy consumption performance evaluation of double-layer photovoltaic exterior window buildings using improved TOPSIS method of grey relational[J]．Journal of Huaqiao University (Natural Science)，2021，42(6)：792-799．
7	王志民，杨劲松，佟强，等．高寒地区独立光伏系统降额设计与性能评价[J]．太阳能学报，2018，39(12)：3384-3391．
	WANG Z M， YANG J S， TONG Q，et al ．Derating design and performance evaluation of stand alone pv system in paramos[J]．Acta Energiae Solaris Sinica，2018，39(12)：3384-3391．
8	白恺，崔正湃，李智，等．基于SCADA和分层递进的并网光伏电站状态评估技术研究[C]//中国电机工程学会电力系统自动化专业委员会三届五次会议暨学术交流会．北京，中国点击工程学会，2015：180-188．
	BAI K， CUI Z P， LI Z，et al ．Research on condition assessment technology of grid-connected photovoltaic power plants based on SCADA and layered and progressive[C]//The fifth meeting of the 3rd session of the Power System Automation Professional Committee of China Electrical Engineering Society and Academic Exchange Meeting．Beijing，CSEE，2015：180-188．
9	武文栋，施保华，郑传良，等．基于改进麻雀搜索算法优化RBF神经网络的光伏阵列故障诊断[J]．智慧电力，2023，51(2)：77-83．
	WU W D， SHI B H， ZHENG C L，et al ．Fault diagnosis of photovoltaic array based on improved sparrow search algorithm optimized RBF neural network[J]．Smart Power，2023，51(2)：77-83．
10	杨帅，曾文伟，杨凌云，等．基于GOA-SVM的光伏阵列故障诊断方法研究[J]．电力科学与技术学报，2024，39(5)：172-180．
	YANG S， ZENG W W， YANG L Y，et al ．Research on fault diagnosis method for photovoltaic array based on GOA-SVM[J]．Journal of Electric Power Science and Technology，2024，39(5)：172-180．
11	王小宇，刘波，孙凯，等．光伏阵列故障诊断技术综述[J]．电工技术学报，2024，39(20)：6526-6543．
	WANG X Y， LIU B， SUN K，et al ．A review of photovoltaic array fault diagnosis technology[J]．Transactions of China Electrotechnical Society，2024，39(20)：6526-6543．
12	杨宏超．基于光伏组件内部参数辨识的故障诊断模型研究[D]．南昌：南昌航空大学，2018．
	YANG H C ．Research on fault diagnosis model based on identification of internal parameters of photovoltaic modules[D]．Nanchang：Nanchang Hangkong University，2018．
13	郑杰，陈思铭，钟柳，等．海南大型并网光伏电站性能质量评价与分析[J]．广东科技，2013，22(24)：149-150．
	ZHENG J， CHEN S M， ZHONG L，et al ．Guangdong Science & Technology，2013，22(24)：149-150．
14	刘家鼎．国内早期光伏电站的系统效率及系统效率损失分析[J]．太阳能，2021(4)：64-69．
	LIU J D ．Analysis of performance ratio and performance ratio loss of early PV power stations in China[J]．Solar Energy，2021(4)：64-69．
15	杨宏，李文栋，赵振兵．基于光滑自助法的风功率预测误差核密度建模方法[J]．可再生能源，2021，39(4)：494-500．
	YANG H， LI W D， ZHAO Z B ．Kernel density estimation of wind power forecast error based on smooth bootstrap method[J]．Renewable Energy Resources，2021，39(4)：494-500．
16	PAN J， ZOU Z， SUN S，et al ．Research on output distribution modeling of photovoltaic modules based on kernel density estimation method and its application in anomaly identification[J]．Solar Energy，2022，235：1-11． doi:10.1016/j.solener.2022.02.020
17	TONG Q， LI D H， REN X，et al ．Classification method of photovoltaic array operating state based on nonparametric estimation and 3σ Method[J]．Sustainability，2023，15：7769-7780． doi:10.3390/su15107769
18	陈建国，张金剑，虞立涛．基于温度修正的光伏电站发电性能评价指标对比分析[J]．华电技术，2016，38(5)：73-76．
	CHEN J G， ZHANG J J， YU L T ．Comparative analysis of photovoltaic power plant power generation performance evaluation indicators based on temperature correction[J]．Huadian Technology，2016，38(5)：73-76．

[1]	Chen YANG, Fengjie NIU, Maolin HAN, Ning ZHOU, Dingxuan ZHOU. Research on Fault Diagnosis Method of Photovoltaic Arrays Based on Improved Grey Wolf Algorithm Optimized Extreme Learning Machine [J]. Power Generation Technology, 2025, 46(1): 72-82.
[2]	Kang YANG, Lanqing LI, Yifeng LI, Dongkuo SONG, Bolun WANG, Jin CHEN, Xia ZHOU, Yu SHAN. A Novel Distributed Photovoltaic Output Interval Prediction Method [J]. Power Generation Technology, 2024, 45(4): 684-695.
[3]	Yongjie ZHONG, Ling JI, Jingxia LI, Jianxun ZUO, Zidong WANG, Shiwei WU. System Framework and Comprehensive Functions of Intelligent Operation Management and Control Platform for Virtual Power Plant [J]. Power Generation Technology, 2023, 44(5): 656-666.
[4]	Fang FANG, Dongyang LIANG, Yajuan LIU, Yang HU, Jizhen LIU. Key Technologies for Intelligent Control and Operation and Maintenance of Offshore Wind Power [J]. Power Generation Technology, 2022, 43(2): 175-185.
[5]	Yuanyuan SHI, Cong DONG, Wenchao WANG, Xing ZHU, Yongli HUANG, Ming DING, Peng YANG. Experimental and Simulation Study of Series-Parallel Thermoelectric Power Generation Model Based on Liquid Medium [J]. Power Generation Technology, 2021, 42(5): 614-621.
[6]	Limin WANG,Guosheng YANG,Rongrong ZHAN,Wenhuan WANG,Yanfei LI. State Evaluation of Relay Protection Based on Multi-Source Information Fusion and Its Application in the Life Cycle Management of Equipment [J]. Power Generation Technology, 2019, 40(3): 300-306.