考虑参数权重与分层映射的光伏组件健康程度检测

doi:10.19595/j.cnki.1000-6753.tces.230896

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Abstract Traditional health status detection of photovoltaic (PV) modules generally includes only two states: health or fault, which is not conducive to fault prediction and system maintenance. Recently, some methods have been presented to detect the health degree of PV modules. However, it is difficult to detect the health status of PV modules in a low-irradiation environment, and the effect of the parameter weights and natural attenuation is ignored. This paper proposes a detection method to characterize the health of PV modules by calculating the health index through parameter deterioration degree and parameter weights.
Firstly, according to the I-V curve of the PV modules, the health parameters are identified by the manta ray foraging optimization (MRFO) algorithm, including the photo generated current I_ph, series resistance R_s, and parallel resistance R_sh. Secondly, setting the high/low irradiation reference state, according to the irradiance measured when the I-V curve was acquired, the parameters identification results are hierarchically mapped, which improves the accuracy of parameter extraction in a low irradiation environment. Thirdly, the expected value of parameters after n years of natural attenuation is obtained by establishing the natural attenuation model of PV modules and used as a reference for calculating the parameter deterioration degree. Finally, taking the health parameters of PV modules in various health states as samples, the parameter weights are calculated by the entropy weight method, and the health index of the range [0, 1] is calculated by combining the parameter deterioration degree. The smaller the index, the healthier the PV module. Different parameters influence the health status of PV modules, and the reliability of detection results can be increased by considering the parameter weights.
Simulation and experimental results show that the convergence speed of parameter identification using MRFO is fast, and the identification error is as low as 4.647×10^-4. The parameter identification results are mapped in different ways. When the irradiance is 200 W/m², the hierarchical mapping reduces the root mean square error by 89.95% compared with the traditional mapping method. The health detection results of PV modules under a low irradiation state are consistent with the reference value using hierarchical mapping. The natural attenuation model provides the natural attenuation parameters as the expected value of parameters, combined with the parameter extraction value to calculate the parameter deterioration. The calculation results show that R_sh is prone to large detection errors. However, the parameter weight of R_sh is only 7.187%, which has little impact on the health status. The health index with parameter weights is in line with expectations. With the increase of abnormal attenuation degree of experimental modules’ characteristic parameters, the health index gradually increases from 0.015 to 0.962, indicating that the health of the PV modules is decreasing.
The following conclusions can be drawn. (1) MRFO has a fast convergence speed and strong optimization ability, which is appropriate for parameter identification. (2) Hierarchical mapping can effectively improve the accuracy of parameter extraction and health detection of PV modules in low-radiation environments. (3) The natural attenuation model of PV modules calculates the parameters’ expected value according to the operation year of modules, which avoids natural attenuation affecting the accuracy of parameter deterioration degree. (4) The parameter weights consider the influence of parameters on the health status, which effectively improves the reliability and accuracy of the health detection of PV modules.

Key words： Photovoltaic modules identification of health parameters hierarchical mapping natural attenuation model health parameter weights

Received: 09 June 2023

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TM615

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	Wu Chunhua
	Yi Yuan
	Li Zhihua
	Wang Fei

Cite this article:

Wu Chunhua,Yi Yuan,Li Zhihua等. Health Detection of Photovoltaic Modules Considering Parameter Weights and Hierarchical Mapping[J]. Transactions of China Electrotechnical Society, 2024, 39(15): 4856-4867.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230896 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I15/4856

[1] 王尧, 马桐桐, 赵宇初, 等. 基于电磁辐射时延估计的串联光伏直流电弧故障定位方法[J]. 电工技术学报, 2023, 38(8): 2233-2243.
Wang Yao, Ma Tongtong, Zhao Yuchu, et al.Series DC arc-fault location method based on electro- magnetic radiation delay estimation for photovoltaic systems[J]. Transactions of China Electrotechnical Society, 2023, 38(8): 2233-2243.
[2] 刘晓艳, 王珏, 姚铁锤, 等. 基于卫星遥感的超短期分布式光伏功率预测[J]. 电工技术学报, 2022, 37(7): 1800-1809.
Liu Xiaoyan, Wang Jue, Yao Tiechui, et al.Ultra short-term distributed photovoltaic power prediction based on satellite remote sensing[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1800-1809.
[3] 顾崇寅, 徐潇源, 王梦圆, 等. 基于CatBoost算法的光伏阵列故障诊断方法[J]. 电力系统自动化, 2023, 47(2): 105-114.
Gu Chongyin, Xu Xiaoyuan, Wang Mengyuan, et al.CatBoost algorithm based fault diagnosis method for photovoltaic arrays[J]. Automation of Electric Power Systems, 2023, 47(2): 105-114.
[4] 丁坤, 陈富东, 翁帅, 等. 基于Ⅰ-Ⅴ特性灰色关联分析的光伏阵列健康状态评估[J]. 电网技术, 2021, 45(8): 3087-3095.
Ding Kun, Chen Fudong, Weng Shuai, et al.Health state evaluation of photovoltaic array based on Ⅰ-Ⅴ characteristics and grey relational analysis[J]. Power System Technology, 2021, 45(8): 3087-3095.
[5] Han C, Lee H.A field-applicable health monitoring method for photovoltaic system[J]. Reliability Engineering & System Safety, 2019, 184: 219-227.
[6] Ding Kun, Chen Xiang, Weng Shuai, et al.Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance[J]. Energy, 2023, 262: 125539.
[7] Kellil N, Aissat A, Mellit A.Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions[J]. Energy, 2023, 263: 125902.
[8] 杨瑞珍, 杜博伦, 何赟泽, 等. 晶体硅光伏电池电磁感应激励红外热辐射缺陷检测与成像技术[J]. 电工技术学报, 2018, 33(增刊2): 321-330.
Yang Ruizhen, Du Bolun, He Yunze, et al.Infrared radiation defect detection and imaging technique under active electromagnetic induction excitation for crystalline silicon photovoltaic cells[J]. Transactions of China Electrotechnical Society, 2018, 33(S2): 321-330.
[9] 赵靖英, 吴晶晶, 张雪辉, 等. 基于萤火虫扰动麻雀搜索算法-极限学习机的光伏阵列故障诊断方法研究[J]. 电网技术, 2023, 47(4): 1612-1625.
Zhao Jingying, Wu Jingjing, Zhang Xuehui, et al.Fault diagnosis of photovoltaic arrays based on sparrow search algorithm with firefly perturbation- extreme learning machine[J]. Power System Technology, 2023, 47(4): 1612-1625.
[10] He Zengxiang, Chu Pengpeng, Li Chenxi, et al.Compound fault diagnosis for photovoltaic arrays based on multi-label learning considering multiple faults coupling[J]. Energy Conversion and Management, 2023, 279: 116742.
[11] 陈凌, 韩伟, 张经炜. 基于数据融合的光伏组件故障诊断[J]. 电网技术, 2017, 41(6): 1864-1872.
Chen Ling, Han Wei, Zhang Jingwei.PV module fault diagnosis based on data fusion[J]. Power System Technology, 2017, 41(6): 1864-1872.
[12] 周亮, 武美娜, 胡安. 局部遮挡下光伏阵列的快速建模及极值点分布特征研究[J]. 电工技术学报, 2021, 36(增刊2): 572-581.
Zhou Liang, Wu Meina, Hu An.Fast modeling and analysis of power peaks characteristics of PV arrays under partial shading conditions[J]. Transactions of China Electrotechnical Society, 2021, 36(S2): 572-581.
[13] 李畸勇, 张伟斌, 赵新哲, 等. 改进鲸鱼算法优化支持向量回归的光伏最大功率点跟踪[J]. 电工技术学报, 2021, 36(9): 1771-1781.
Li Jiyong, Zhang Weibin, Zhao Xinzhe, et al.Global maximum power point tracking for PV array based on support vector regression optimized by improved whale algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1771-1781.
[14] 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 Chunhua, Yu Xueying, Li Zhihua, et al.Health state diagnosis of photovoltaic modules based on FCM and Gaussian membership[J]. Power System Technology, 2022, 46(5): 1887-1896.
[15] Huang Chao, Wang Long.Simulation study on the degradation process of photovoltaic modules[J]. Energy Conversion and Management, 2018, 165: 236-243.
[16] 程泽, 巩力, 刘艳莉. 光学损失故障对单晶硅光伏电池参数的影响[J]. 电工技术学报, 2016, 31(17): 217-223.
Cheng Ze, Gong Li, Liu Yanli.The influence of optical losses on monocrystalline silicon solar cells parameters[J]. Transactions of China Electrotechnical Society, 2016, 31(17): 217-223.
[17] Houssein E H, Zaki G N, Diab A A Z, et al. An efficient Manta Ray Foraging optimization algorithm for parameter extraction of three-diode photovoltaic model[J]. Computers & Electrical Engineering, 2021, 94: 107304.
[18] 邱纯. 任意辐照度与温度条件下光伏系统输出特性建模[D]. 武汉: 华中科技大学, 2011.
[19] Messenger R A, Ventre J.Photovoltaic Systems Engineering Second Edi. Taylor&Francis, 2004.
[20] 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.
[21] 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 Zhihua, Ma Haoqiang, Wu Chunhua, et al.Diagnosing the aging degree of photovoltaic modules based on three parameters[J]. Proceedings of the CSEE, 2022, 42(9): 3327-3338.
[22] 中华人民共和国工业和信息化部公告—光伏制造行业规范条件[J]. Solar Energy, 2013(20): 6-8.
[23] Lillo-Sánchez L, López-Lara G, Vera-Medina J, et al.Degradation analysis of photovoltaic modules after operating for 22 years. A case study ith comparisons[J]. Solar Energy, 2021, 222: 84-94.
[24] da Fonseca J E F, de Oliveira F S, Massen Prieb C W, et al. Degradation analysis of a photovoltaic generator after operating for 15 years in southern Brazil[J]. Solar Energy, 2020, 196: 196-206.
[25] Townsend T U.A method for estimating the long- term performance of direct-coupled photovoltaic systems[D]. Madison: University of Wisconsin, 1989.