一种光伏组件健康状况的动态评估方法

陈瀚宇; 李智华; 汪飞; 易苑; 伍楠

doi:10.19912/j.0254-0096.tynxb.2024-2237

PDF(2300 KB)

太阳能学报 ›› 2026, Vol. 47 ›› Issue (4) : 719-728. DOI: 10.19912/j.0254-0096.tynxb.2024-2237

一种光伏组件健康状况的动态评估方法

陈瀚宇, 李智华, 汪飞, 易苑, 伍楠

作者信息 +

A DYNAMIC ASSESSMENT METHOD FOR HEALTH STATUS OF PHOTOVOLTAIC MODULES

Chen Hanyu, Li Zhihua, Wang Fei, Yi Yuan, Wu Nan

Author information +

文章历史 +

摘要

针对光伏组件健康状态的评估问题,提出一种基于健康指标动态阈值选取和动态权重调整机制的评估方法。将组件光生电流I_ph、等效串联电阻R_s和等效并联电阻R_p作为健康参数,基于组件I-V曲线、利用蜣螂算法进行健康参数辨识。结合组件出厂标准和服役年限,建立光伏组件动态健康模型。引入动态权重调整机制,确保准确识别低权重参数表征的故障。仿真和实验结果证明,所提出的方法能够较好地评估光伏组件的健康状况,并对静态权重方法下评估错误的故障信息进行有效诊断。

Abstract

To address the need of state estimation for photovoltaic （PV） module health, a method based on dynamic threshold selection and dynamic weight adjustment mechanism for health indicators is proposed. The photogenerated currentI_ph, equivalent series resistanceR_s, and equivalent parallel resistance R_p are taken as health parameters, and they are extracted using the dung beetle algorithm based on the I-V curve of the module. A dynamic health model is developed by integrating factory standards and operational aging effects. A dynamic weight adjustment mechanism is introduced to ensure accurate identification of faults characterized by low-weight parameters. Simulations and experiments confirm that the proposed method improves state estimation reliability compared to static weight approaches, enabling precise identification of misdiagnosed faults.

导出引用

陈瀚宇, 李智华, 汪飞, 易苑, 伍楠. 一种光伏组件健康状况的动态评估方法[J]. 太阳能学报. 2026, 47(4): 719-728 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2237

Chen Hanyu, Li Zhihua, Wang Fei, Yi Yuan, Wu Nan. A DYNAMIC ASSESSMENT METHOD FOR HEALTH STATUS OF PHOTOVOLTAIC MODULES[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 719-728 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2237

中图分类号： TM615

参考文献

[1] TIAN J, CULLEY S A, MAIER H R, et al.Is renewable energy sustainable? Potential relationships between renewable energy production and the Sustainable Development Goals[J]. NPJ climate action, 2024, 3: 35.
[2] AGHAEI M, FAIRBROTHER A, GOK A, et al.Review of degradation and failure phenomena in photovoltaic modules[J]. Renewable and sustainable energy reviews, 2022, 159: 112160.
[3] PIERDICCA R, PAOLANTI M, FELICETTI A, et al.Automatic faults detection of photovoltaic farms: solAIr, a deep learning-based system for thermal images[J]. Energies, 2020, 13(24): 6496.
[4] PRATT L, GOVENDER D, KLEIN R.Defect detection and quantification in electroluminescence images of solar PV modules using U-Net semantic segmentation[J]. Renewable energy, 2021, 178: 1211-1222.
[5] 匡兴泽. 基于机器视觉的太阳能电池片缺陷检测算法研究[D]. 武汉: 武汉理工大学, 2016.
KUANG X Z.The algorithm research of defects detection for solar cells base on machine vision[D]. Wuhan: Wuhan University of Technology, 2016.
[6] 张臻, 王磊, 秦志光, 等. 双面光伏组件发电性能影响因素理论与实验研究[J]. 太阳能学报, 2022, 43(3): 171-179.
ZHANG Z, WANG L, QIN Z G, et al.Theoretical and experimental study on influencing factors of bifacial photovoltaic modules field performance[J]. Acta energiae solaris sinica, 2022, 43(3): 171-179.
[7] JORDAN D C, DELINE C, KURTZ S R, et al.Robust PV degradation methodology and application[J]. IEEE journal of photovoltaics, 2018, 8(2): 525-531.
[8] HUANG C, WANG L.Simulation study on the degradation process of photovoltaic modules[J]. Energy conversion and management, 2018, 165: 236-243.
[9] 赵丽琴, 刘昶, 易发胜. 基于动态权重的设备健康状态评估方法[J]. 计算机系统应用, 2020, 29(9): 198-204.
ZHAO L Q, LIU C, YI F S.Assessment method of equipment health state based on dynamic weight[J]. Computer systems & applications, 2020, 29(9): 198-204.
[10] 苏建徽, 余世杰, 赵为, 等. 硅太阳电池工程用数学模型[J]. 太阳能学报, 2001, 22(4): 409-412.
SU J H, YU S J, ZHAO W, et al.Investigation on engineering analytical model of silicon solar cells[J]. Acta energiae solaris sinica, 2001, 22(4): 409-412.
[11] COLVIN D J, IQBAL N, YERGER J H, et al.Degradation of monocrystalline silicon photovoltaic modules from a 10-year-old rooftop system in Florida[J]. IEEE journal of photovoltaics, 2023, 13(2): 275-282.
[12] 李智华, 马浩强, 吴春华, 等. 基于三参数的光伏组件老化程度诊断[J]. 中国电机工程学报, 2022, 42(9): 3327-3338.
LI Z H, MA H Q, WU C H, et al.Diagnosing the aging degree of photovoltaic modules based on three parameters[J]. Proceedings of the CSEE, 2022, 42(9): 3327-3338.
[13] YANG B, WANG J B, ZHANG X S, et al.Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification[J]. Energy conversion and management, 2020, 208: 112595.
[14] 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 W, WANG H H, CHEN L, et al.Research on parameter fitting and output characteristics of PV module[J]. Electric power automation equipment, 2015, 35(9): 100-107.
[15] XUE J K, SHEN B.Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The journal of supercomputing, 2023, 79(7): 7305-7336.
[16] DE SOTO W, KLEIN S A, BECKMAN W A.Improvement and validation of a model for photovoltaic array performance[J]. Solar energy, 2006, 80(1): 78-88.
[17] 李智华, 张宇浩, 吴春华, 等. 基于动态指标的光伏组件健康程度诊断[J]. 电网技术, 2024, 48(2): 597-606.
LI Z H, ZHANG Y H, WU C H, et al.Health diagnosis of photovoltaic modules based on dynamic health criteria[J]. Power system technology, 2024, 48(2): 597-606.
[18] 中华人民共和国工业和信息化部. 光伏制造行业规范条件(2015年本)[EB/OL]. http://www.miit.gov.cn/zwgk/zcwj/wjfb/gg/art.
MINISTRY OF INDUSTRY AND INFORMATION TECHNOLOGY. Industry specifications of photovoltaic manufacturing(2015)[EB/OL]. http://www.miit.gov.cn/zwgk/zcwj/wjfb/gg/art.
[19] 中华人民共和国工业和信息化部. 光伏制造行业规范条件(2018年本)[EB/OL]. https://wap.miit.gov.cn/zwgk/zcwj/wjfb/gg/art.
MINISTRY OF INDUSTRY AND INFORMATION TECHNOLOGY. Industry specifications of photovoltaic manufacturing(2018)[EB/OL]. https://wap.miit.gov.cn/zwgk/zcwj/wjfb/gg/art.
[20] 中华人民共和国工业和信息化部. 光伏制造行业规范条件(2021年本)[EB/OL]. https://wap.miit.gov.cn/zwgk/zcwj/wjfb/gg/art/2021/art_24b585fce0f64c98be8efe14e0446458.html.
MINISTRY OF INDUSTRY AND INFORMATION TECHNOLOGY. Industry specifications of photovoltaic manufacturing(2021)[EB/OL]. https://wap.miit.gov.cn/zwgk/zcwj/wjfb/gg/art.
[21] 中华人民共和国工业和信息化部. 光伏制造行业规范条件(2024年本)(征求意见稿)[EB/OL]. https://www.miit.gov.cn/jgsj/dzs/gzdt/art.
MINISTRY OF INDUSTRY AND INFORMATION TECHNOLOGY. Industry specifications of photovoltaic manufacturing(2024, Draft for opinions)[EB/OL]. https://www.miit.gov.cn/jgsj/dzs/gzdt/art.
[22] LILLO-SÁNCHEZ L, LÓPEZ-LARA G, VERA-MEDINA J, et al. Degradation analysis of photovoltaic modules after operating for 22 years. A case study with comparisons[J]. Solar energy, 2021, 222: 84-94.
[23] 吴春华, 易苑, 李智华, 等. 考虑参数权重与分层映射的光伏组件健康程度检测[J]. 电工技术学报, 2024, 39(15): 4856-4867.
WU C H, YI Y, LI Z H, et al.Health detection of photovoltaic modules considering parameter weights and hierarchical mapping[J]. Transactions of China Electrotechnical Society, 2024, 39(15): 4856-4867.
[24] 彭炎亮, 李汪根, 刘娇, 等. 基于动态权重和模糊综合评判法的健康评估模型[J]. 计算机系统应用, 2017, 26(1): 37-43.
PENG Y L, LI W G, LIU J, et al.Health assessment model based on dynamic weight and fuzzy comprehensive evaluation method[J]. Computer systems & applications, 2017, 26(1): 37-43.
[25] 丁坤, 刘振飞, 高列, 等. 基于主成分分析和马氏距离的光伏系统健康状态研究[J]. 可再生能源, 2017, 35(1): 1-7.
DING K, LIU Z F, GAO L, et al.Research on photovoltaic system health state based on PCA-MD method[J]. Renewable energy resources, 2017, 35(1): 1-7.
[26] 张治, 王新宇, 王林. 基于麻雀搜索算法的光伏阵列参数辨识[J]. 电气传动, 2022, 52(13): 75-80.
ZHANG Z, WANG X Y, WANG L.Photovoltaic array parameter identification based on sparrow search algorithm[J]. Electric drive, 2022, 52(13): 75-80.
[27] 吴春华, 俞薛颖, 李智华, 等. 基于FCM与高斯隶属度的光伏组件健康状态诊断[J]. 电网技术, 2022, 46(5): 1887-1896.
WU C H, YU X Y, LI Z H, et al.Health state diagnosis of photovoltaic modules based on FCM and Gaussian membership[J]. Power system technology, 2022, 46(5): 1887-1896.