A Review of Photovoltaic Array Fault Diagnosis Technology
Wang Xiaoyu1, Liu Bo1, Sun Kai2, Zhao Jian1, Chen Lei1
1. College of Electrical Engineering Shanghai University of Electric Power Shanghai 200082 China; 2. State Key Laboratory of Power System Operation and Control Tsinghua University Beijing 100084 China
Abstract：A photovoltaic array is an integral part of a photovoltaic power generation system. Due to its long exposure to the external environment, it is prone to different types and degrees of faults. The fault diagnosis of the photovoltaic array is significant to the safe, reliable, and economic operation of photovoltaic power generation systems. Therefore, this paper summarizes the frontier diagnosis technology of photovoltaic array faults. Firstly, the common fault types and causes of photovoltaic arrays are introduced, including the faults between photovoltaic modules and single-module faults. Furthermore, the operating mechanism of the photovoltaic module is described by a single-diode equivalent circuit model. Based on the model, the voltage and current under different fault types change significantly. The variation trend of voltage and current curves divides these faults into non-mismatch and mismatch faults. For non-mismatched faults, the I-V curve does not change significantly, such as PID faults and diode short circuits. For mismatched faults, the I-V curve has a slope or step-shape trend, such as shadows and hot spots. Secondly, based on the mechanism characteristics and external representation of photovoltaic array faults, this paper summarizes photovoltaic array fault diagnosis methods into two categories: vision and imaging diagnosis methods and electrical characteristic parameter diagnosis methods. Related methods are studied to achieve fault diagnosis by obtaining image and data information through auxiliary detection equipment. Vision and imaging diagnosis methods consist of infrared thermal imaging, visible imaging, electroluminescence, photoluminescence, and machine-learning image diagnosis methods. Electrical characteristic parameter diagnosis methods include I-V curve, physical detection, power loss analysis, voltage-current, and machine learning classification methods. This paper summarizes and analyzes the existing domestic and foreign research. Specifically, the electroluminescence and photoluminescence imaging methods are used to diagnose crack and micro-crack faults. The infrared thermal imaging method diagnoses hot spot faults. The machine-learning image detection method achieves object detection and semantic segmentation of the fault points through the acquired image information. In addition, the I-V curve method is used to analyze and judge the characteristics and trends of the curve, such as shadows and hot spots. The physical detection method diagnoses different modules in photovoltaic arrays, such as grounding and short-circuit faults. Using the equivalent circuit model, the machine-learning method diagnoses open-circuit, line-line, and short-circuit faults based on massive data set. Finally, it is pointed out that the establishment of photovoltaic module fault models, the improvement of fault feature recognition methods, the application of artificial intelligence technology, the health management of photovoltaic array operation state, and the optimization of fault diagnosis strategies need to be further studied in the future. It aims to improve the fault diagnosis capabilities of photovoltaic arrays and provide a technical basis for intelligent operation and maintenance of photovoltaic systems.
[1] 李军徽, 安晨宇, 李翠萍, 等. 计及调峰市场交易的储能-新能源-火电多目标优化调度[J]. 电工技术学报, 2023, 38(23): 6391-6406. Li Junhui, An Chenyu, Li Cuiping, et al.Multi- objective optimization scheduling method considering peak regulating market transactions for energy storage-new energy-thermal Power[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6391-6406. [2] 朱晓荣, 刘亚维. 基于元件-系统分级优化的光伏电站维护策略[J]. 电力系统自动化, 2020, 44(8): 92-99. Zhu Xiaorong, Liu Yawei.Maintenance strategy of photovoltaic power station based on component- system classification optimization[J]. Automation of Electric Power Systems, 2020, 44(8): 92-99. [3] 徐瑞东, 陈昊, 胡义华, 等. 基于高斯过程的光伏阵列故障定位[J]. 电工技术学报, 2013, 28(6): 249-256. Xu Ruidong, Chen Hao, Hu Yihua, et al.Fault location of photovoltaic array based on Gaussian process[J]. Transactions of China Electrotechnical Society, 2013, 28(6): 249-256. [4] 曹宇. 中国光伏十年复盘: 多少电站已近中年[EB/OL]. (2019-07-08) https://solar.huawei.com/cn/news-room/cn/2019/cn_20190708. [5] 于南. 管理层出手整治光伏电站质量欲为应用端打通融资[EB/OL]. (2014-10-06) http://energy.people. com.cn/n/2014/1016/c71661-25844582.html. [6] 桂建生. 基于多层次模糊评价方法的太阳能投资项目风险评价研究[D]. 北京: 华北电力大学, 2016. [7] Madeti S R, Singh S N.A comprehensive study on different types of faults and detection techniques for solar photovoltaic system[J]. Solar Energy, 2017, 158: 161-185. [8] Haque A, Bharath K V S, Ali Khan M, et al. Fault diagnosis of photovoltaic modules[J]. Energy Science & Engineering, 2019, 7(3): 622-644. [9] 王元章, 李智华, 吴春华. 光伏系统故障诊断方法综述[J]. 电源技术, 2013, 37(9): 1700-1705. Wang Yuanzhang, Li Zhihua, Wu Chunhua.Fault diagnosis technologies for photovoltaic system[J]. Chinese Journal of Power Sources, 2013, 37(9): 1700-1705. [10] Pillai D S, Rajasekar N.A comprehensive review on protection challenges and fault diagnosis in PV systems[J]. Renewable and Sustainable Energy Reviews, 2018, 91: 18-40. [11] 戈鹏. 光伏组件加工工艺与质量控制[J]. 机械管理开发, 2022, 37(1): 271-273. Ge Peng.Photovoltaic module processing process and quality control[J]. Mechanical Management and Development, 2022, 37(1): 271-273. [12] de Oliveira M C C, Diniz Cardoso A S A, Viana M M, et al. The causes and effects of degradation of encapsulant ethylene vinyl acetate copolymer (EVA) in crystalline silicon photovoltaic modules: a review[J]. Renewable and Sustainable Energy Reviews, 2018, 81: 2299-2317. [13] Jordan D C, Silverman T J, Wohlgemuth J H, et al.Photovoltaic failure and degradation modes[J]. Progress in Photovoltaics: Research and Applications, 2017, 25(4): 318-326. [14] Santhakumari M, Sagar N.A review of the environmental factors degrading the performance of silicon wafer-based photovoltaic modules: failure detection methods and essential mitigation tech- niques[J]. Renewable and Sustainable Energy Reviews, 2019, 110: 83-100. [15] Kumar M, Kumar A.Performance assessment and degradation analysis of solar photovoltaic tech- nologies: a review[J]. Renewable and Sustainable Energy Reviews, 2017, 78: 554-587. [16] Li Guiqiang, Jin Yi, Akram M W, et al.Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions-a review[J]. Renewable and Sustainable Energy Reviews, 2018, 81: 840-873. [17] Deng Shifeng, Zhang Zhen, Ju Chenhui, et al.Research on hot spot risk for high-efficiency solar module[J]. Energy Procedia, 2017, 130: 77-86. [18] Triki-Lahiani A, Bennani-Ben Abdelghani A, Slama- Belkhodja I. Fault detection and monitoring systems for photovoltaic installations: a review[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 2680-2692. [19] van Deelen J, Barink M, Klerk L, et al. Efficiency loss prevention in monolithically integrated thin film solar cells by improved front contact[J]. Progress in Photovoltaics: Research and Applications, 2015, 23(4): 498-506. [20] Dhimish M.Micro cracks distribution and power degradation of polycrystalline solar cells wafer: observations constructed from the analysis of 4000 samples[J]. Renewable Energy, 2020, 145: 466-477. [21] Haase F, Käsewieter J, Nabavi S R, et al.Fracture probability,crack patterns,and crack widths of multicrystalline silicon solar cells in PV modules during mechanical loading[J]. IEEE Journal of Photovoltaics, 2018, 8(6): 1510-1524. [22] Dhimish M, Holmes V, Mather P.Novel photovoltaic micro crack detection technique[J]. IEEE Transa- ctions on Device and Materials Reliability, 2019, 19(2): 304-312. [23] Dhimish M, D'Alessandro V, Daliento S. Investi- gating the impact of cracks on solar cells performance: analysis based on nonuniform and uniform crack distributions[J]. IEEE Transactions on Industrial Informatics, 2022, 18(3): 1684-1693. [24] Datsiou K C, Overend M.Artificial ageing of glass with sand abrasion[J]. Construction and Building Materials, 2017, 142: 536-551. [25] 樊静, 钱政, 王婧怡. 基于湿热试验的光伏组件功率衰减与使用寿命研究[J]. 计算机测量与控制, 2019, 27(4): 249-253. Fan Jing, Qian Zheng, Wang Jingyi.Study on power degradation and service lifetime of photovoltaic modules based on damp-heat test[J]. Computer Measurement & Control, 2019, 27(4): 249-253 [26] 黄岳文, 曹华翔, 刘帅, 等. 单晶硅光伏组件性能衰减分析[J]. 电源技术, 2019, 43(4): 641-645. Huang Yuewen, Cao Huaxiang, Liu Shuai, et al.Degradation analysis of monocrystalline silicon PV modules[J]. Chinese Journal of Power Sources, 2019, 43(4): 641-645. [27] 孙晓, 怀朝君, 胡旦, 等. 晶体硅光伏组件功率衰减与评估方法研究[J]. 太阳能学报, 2016, 37(6): 1373-1378. Sun Xiao, Huai Chaojun, Hu Dan, et al.Study of power degradation and evaluate process of crystalline silicon pv modules[J]. Acta Energiae Solaris Sinica, 2016, 37(6): 1373-1378. [28] 马铭遥, 徐君, 张志祥. 光伏发电系统智能化故障诊断技术[M]. 北京: 机械工业出版社, 2022. [29] Yilmaz P, Schmitz J, Theelen M.Potential induced degradation of CIGS PV systems: a literature review[J]. Renewable and Sustainable Energy Reviews, 2022, 154: 111819. [30] 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. [31] 陈佳凝, 魏霞, 王维庆. 基于多工况的光伏阵列的建模及输出特性分析[J]. 电源技术, 2022, 46(9): 1038-1042. Chen Jianing, Wei Xia, Wang Weiqing.Modeling and output characteristic analysis of photovoltaic array based on multiple working conditions[J]. Chinese Journal of Power Sources, 2022, 46(9): 1038-1042. [32] 何志鹏, 李伟林, 邓云坤, 等. 低压交流串联故障电弧辨识方法[J]. 电工技术学报, 2023, 38(10): 2806-2817. He Zhipeng, Li Weilin, Deng Yunkun, et al.The detection of series AC arc fault in low-voltage distribution system[J]. Transactions of China Electro- technical Society, 2023, 38(10): 2806-2817. [33] 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. [34] Lo Brano V, Orioli A, Ciulla G, et al.An improved five-parameter model for photovoltaic modules[J]. Solar Energy Materials and Solar Cells, 2010, 94(8): 1358-1370. [35] 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. [36] 马铭遥, 张志祥, 刘恒, 等. 基于I-V特性分析的晶硅光伏组件故障诊断[J]. 太阳能学报, 2021, 42(6): 130-137. Ma Mingyao, Zhang Zhixiang, Liu Heng, et al.Fault diagnosis of crystalline silicon photovoltaic module based on I-V characteristic analysis[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 130-137. [37] Liu Yongjie, Ding Kun, Zhang Jingwei, et al.Fault diagnosis approach for photovoltaic array based on the stacked auto-encoder and clustering with I-V curves[J]. Energy Conversion and Management, 2021, 245: 114603. [38] Ma Mingyao, Zhang Zhixiang, Yun Ping, et al.Photovoltaic module current mismatch fault diagnosis based on I-V data[J]. IEEE Journal of Photovoltaics, 2021, 11(3): 779-788. [39] 沈文忠. 太阳能光伏技术与应用[M]. 上海: 上海交通大学出版社, 2013. [40] Stromer D, Vetter A, Oezkan H C, et al.Enhanced crack segmentation (eCS): a reference algorithm for segmenting cracks in multicrystalline silicon solar cells[J]. IEEE Journal of Photovoltaics, 2019, 9(3): 752-758. [41] Yang Ruizhen, Du Bolun, Duan Puhong, et al.Electromagnetic induction heating and image fusion of silicon photovoltaic cell electrothermography and electroluminescence[J]. IEEE Transactions on Indu- strial Informatics, 2020, 16(7): 4413-4422. [42] Dhimish M, Holmes V, Mather P, et al.Development of 3D graph-based model to examine photovoltaic micro cracks[J]. Journal of Science: Advanced Materials and Devices, 2018, 3(3): 380-388. [43] 程安琪, 宋昊, 白建波, 等. 晶体硅光伏组件EL测试的缺陷分析[J]. 太阳能, 2018(2): 10-13. Cheng Anqi, Song Hao, Bai Jianbo, et al.Defect analysis of EL test of crystalline silicon photovoltaic module[J]. Solar Energy, 2018(2): 10-13. [44] Zhu Yan, Juhl M K, Trupke T, et al.Photo- luminescence imaging of silicon wafers and solar cells with spatially inhomogeneous illumination[J]. IEEE Journal of Photovoltaics, 2017, 7(4): 1087-1091. [45] 严婷婷, 张光春, 李果华, 等. 光致发光技术在Si基太阳电池缺陷检测中的应用[J]. 半导体技术, 2010, 35(5): 454-457. Yan Tingting, Zhang Guangchun, Li Guohua, et al.Application of photoluminescence for the testing of defects in crystalline silicon based solar cells[J]. Semiconductor Technology, 2010, 35(5): 454-457. [46] Doll B, Hepp J, Hoffmann M, et al.Photo- luminescence for defect detection on full-sized photovoltaic modules[J]. IEEE Journal of Photo- voltaics, 2021, 11(6): 1419-1429. [47] 宋卿尧. 光伏组件热斑检测系统[J]. 电子制作, 2021(18): 93-95. Song Qingyao.Photovoltaic module hot spot detection system[J]. Practical Electronics, 2021(18): 93-95. [48] 王亚丽, 孙坚, 徐红伟. 基于红外成像太阳能板缺陷检测方法研究[J]. 测控技术, 2015, 34(7): 59-61, 66. Wang Yali, Sun Jian, Xu Hongwei.Research and method of defect detection of solar panels based on infrared imaging[J]. Measurement & Control Tech- nology, 2015, 34(7): 59-61, 66. [49] Kirsten Vidal de Oliveira A, Aghaei M, Rüther R. Aerial infrared thermography for low-cost and fast fault detection in utility-scale PV power plants[J]. Solar Energy, 2020, 211: 712-724. [50] Waqar Akram M, Li Guiqiang, Jin Yi, et al.Improved outdoor thermography and processing of infrared images for defect detection in PV modules[J]. Solar Energy, 2019, 190: 549-560. [51] 蒋琳, 苏建徽, 李欣, 等. 基于可见光和红外热图像融合的光伏阵列热斑检测方法[J]. 太阳能学报, 2022, 43(1): 393-397. Jiang Lin, Su Jianhui, Li Xin, et al.Hot spot detection of photovoltaic array based on fusion of visible and infrared thermal images[J]. Acta Energiae Solaris Sinica, 2022, 43(1): 393-397. [52] Rahaman S A, Urmee T, Parlevliet D A.Investigate the impact of environmental and operating conditions of infrared (IR) imaging on infrared thermography of PV modules to identify defects[J]. Solar Energy, 2022, 245: 231-253. [53] He Yunze, Du Bolun, Huang Shoudao.Noncontact electromagnetic induction excited infrared thermo- graphy for photovoltaic cells and modules inspe- ction[J]. IEEE Transactions on Industrial Informatics, 2018, 14(12): 5585-5593. [54] Otamendi U, Martinez I, Quartulli M, et al.Segmentation of cell-level anomalies in electro- luminescence images of photovoltaic modules[J]. Solar Energy, 2021, 220: 914-926. [55] Hong Feng, Song Jie, Meng Hang, et al.A novel framework on intelligent detection for module defects of PV plant combining the visible and infrared images[J]. Solar Energy, 2022, 236: 406-416. [56] Di Tommaso A, Betti A, Fontanelli G, et al.A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle[J]. Renewable Energy, 2022, 193: 941-962. [57] 荣黎明. 基于深度学习的光伏组串热斑检测方法研究[D]. 吉林: 东北电力大学, 2022. [58] Akram M W, Li Guiqiang, Jin Yi, et al.Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning[J]. Solar Energy, 2020, 198: 175-186. [59] 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. [60] 邓堡元, 何赟泽, 王洪金, 等. 光伏电池图像序列的深度学习检测方法[J]. 机械工程学报, 2021, 57(8): 98-106. Deng Baoyuan, He Yunze, Wang Hongjin, et al.Deep learning inspection for photovoltaic cell image sequence[J]. Journal of Mechanical Engineering, 2021, 57(8): 98-106. [61] Jeffrey Kuo C F, Chen S H, Huang Chaoyang. Automatic detection, classification and localization of defects in large photovoltaic plants using unmanned aerial vehicles (UAV) based infrared (IR) and RGB imaging[J]. Energy Conversion and Management, 2023, 276: 116495. [62] 谢颖. 光伏组件热斑故障诊断与分析[D]. 南京: 东南大学, 2021. [63] Bu Chiwu, Liu Tao, Li Rui, et al.Electrical pulsed infrared thermography and supervised learning for PV cells defects detection[J]. Solar Energy Materials and Solar Cells, 2022, 237: 111561. [64] 曾祥超, 张鹤仙, 王水威, 等. 双面光伏组件I-V测试方法研究[J]. 太阳能学报, 2021, 42(3): 370-374. Zeng Xiangchao, Zhang Hexian, Wang Shuiwei, et al.Research on testing methods of I-V characteristics of bifacial PV modules[J]. Acta Energiae Solaris Sinica, 2021, 42(3): 370-374. [65] Zhang Zhixiang, Ma Mingyao, Ma Wenting, et al.A data-driven photovoltaic string current mismatch fault diagnosis method based on I-V curve[J]. Micro- electronics Reliability, 2022, 138: 114705. [66] 刘恒, 马铭遥, 张志祥, 等. 热斑晶硅光伏组件I-V曲线分析及特征模拟研究[J]. 太阳能学报, 2021, 42(4): 239-246. Liu Heng, Ma Mingyao, Zhang Zhixiang, et al.Study on I-V curve analysis and characteristic simulation of silicon photovoltaic module with hot spot[J]. Acta Energiae Solaris Sinica, 2021, 42(4): 239-246. [67] 胡义华, 陈昊, 徐瑞东. 基于电压扫描的光伏阵列故障诊断策略[J]. 中国电机工程学报, 2010, 30(增刊1): 185-191. Hu Yihua, Chen Hao, Xu Ruidong.A type of PV array fault diagnosis strategy based on voltage scan[J]. Proceedings of the CSEE, 2010, 30(S1): 185-191. [68] Li Baojie, Delpha C, Migan-Dubois A, et al.Fault diagnosis of photovoltaic panels using full I-V characteristics and machine learning techniques[J]. Energy Conversion and Management, 2021, 248: 114785. [69] Chen Zhicong, Wu Lijun, Cheng Shuying, 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. [70] Liu Yongjie, Ding Kun, Zhang Jingwei, et al.Intelligent fault diagnosis of photovoltaic array based on variable predictive models and I-V curves[J]. Solar Energy, 2022, 237: 340-351. [71] Fadhel S, Delpha C, Diallo D, et al.PV shading fault detection and classification based on I-V curve using principal component analysis: application to isolated PV system[J]. Solar Energy, 2019, 179: 1-10. [72] Chen Shiqun, Yang Gengjie, Gao Wei, et al.Photo- voltaic fault diagnosis via semisupervised ladder network with string voltage and current measures[J]. IEEE Journal of Photovoltaics, 2021, 11(1): 219-231. [73] Kumar B P, Pillai D S, Rajasekar N, et al.Identi- fication and localization of array faults with optimized placement of voltage sensors in a PV system[J]. IEEE Transactions on Industrial Elec- tronics, 2021, 68(7): 5921-5931. [74] Saleh K A, Hooshyar A, El-Saadany E F, et al. Voltage-based protection scheme for faults within utility-scale photovoltaic arrays[J]. IEEE Transa- ctions on Smart Grid, 2018, 9(5): 4367-4382. [75] Hu Yihua, Zhang Jiangfeng, Cao Wenping, et al.Online two-section PV array fault diagnosis with optimized voltage sensor locations[J]. IEEE Transactions on Industrial Electronics, 2015, 62(11): 7237-7246. [76] Alajmi M, Aljasem O, Ali N, et al.Fault detection and localization in solar photovoltaic arrays framework: hybrid methods of data-analysis and a network of voltage-current sensors[C] //2018 IEEE International Conference on Electro/Information Technology (EIT), Rochester, MI, USA, 2018: 404-410. [77] Takashima T, Yamaguchi J, Ishida M.Disconnection detection using earth capacitance measurement in photovoltaic module string[J]. Progress in Photo- voltaics: Research and Applications, 2008, 16(8): 669-677. [78] Takashima T, Yamaguchi J, Otani K, et al.Experi- mental studies of fault location in PV module strings[J]. Solar Energy Materials and Solar Cells, 2009, 93(6/7): 1079-1082. [79] 孟佳彬, 李智华, 吴春华, 等. 基于SSTDR的光伏系统对地故障检测方法[J]. 太阳能学报, 2020, 41(10): 109-118. Meng Jiabin, Li Zhihua, Wu Chunhua, et al.Ground fault detection method for PV system based on SSTDR[J]. Acta Energiae Solaris Sinica, 2020, 41(10): 109-118. [80] Roy S, Alam M K, Khan F, et al.An irradiance- independent, robust ground-fault detection scheme for PV arrays based on spread spectrum time-domain reflectometry (SSTDR)[J]. IEEE Transactions on Power Electronics, 2018, 33(8): 7046-7057. [81] Edun A S, Kingston S, LaFlamme C, et al. Detection and localization of disconnections in a large-scale string of photovoltaics using SSTDR[J]. IEEE Journal of Photovoltaics, 2021, 11(4): 1097-1104. [82] Alam M K, Khan F H, Johnson J, et al.PV arc-fault detection using spread spectrum time domain refle- ctometry (SSTDR)[C] //2014 IEEE Energy Conversion Congress and Exposition (ECCE), Pittsburgh, PA, USA, 2014: 3294-3300. [83] 苗甲. 光伏系统中直流串联电弧故障的检测研究[D]. 哈尔滨: 哈尔滨工业大学, 2016. [84] 谭秋秋, 石嘉川, 管铭. 基于高频分量周期方差值的故障电弧检测[J]. 建筑电气, 2017, 36(3): 57-62. Tan Qiuqiu, Shi Jiachuan, Guan Ming.Fault arc detection based on high-frequency component periodic variance value[J]. Building Electricity, 2017, 36(3): 57-62. [85] Momoh J A, Button R.Design and analysis of aerospace DC arcing faults using fast Fourier transformation and artificial neural network[C] //2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491), Toronto, ON, Canada, 2004: 788-793. [86] 孙鹏, 郑志成, 高翔. 基于小波分析的故障电弧检测方法[J]. 高压电器, 2012, 48(1): 25-29, 34. Sun Peng, Zheng Zhicheng, Gao Xiang.Arc fault detection based on wavelet analysis[J]. High Voltage Apparatus, 2012, 48(1): 25-29, 34. [87] 熊庆, 肖戎, 汲胜昌, 等. 基于电磁辐射特性的直流电弧检测方法[J]. 高电压技术, 2017, 43(9): 2967-2975. Xiong Qing, Xiao Rong, Ji Shengchang, et al.Detection method for DC arc based on elec- tromagnetic radiation characteristics[J]. High Voltage Engineering, 2017, 43(9): 2967-2975. [88] 王尧, 张彦风, 牛峰, 等. 光伏直流电弧电磁辐射特性分析与测量方法[J]. 电工技术学报, 2019, 34(14): 2913-2921. Wang Yao, Zhang Yanfeng, Niu Feng, et al.Characterization and measurement method of DC arc electromagnetic radiation for photovoltaic systems[J]. Transactions of China Electrotechnical Society, 2019, 34(14): 2913-2921. [89] 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. [90] Chouder A, Silvestre S.Automatic supervision and fault detection of PV systems based on power losses analysis[J]. Energy Conversion and Management, 2010, 51(10): 1929-1937. [91] Dhimish M, Chen Zhicong.Novel open-circuit photovoltaic bypass diode fault detection algorithm[J]. IEEE Journal of Photovoltaics, 2019, 9(6): 1819-1827. [92] Pereira T A, Schmitz L, dos Santos W M, et al. Design of a portable photovoltaic I-V curve tracer based on the DC-DC converter method[J]. IEEE Journal of Photovoltaics, 2021, 11(2): 552-560. [93] Dhimish M, Holmes V.Fault detection algorithm for grid-connected photovoltaic plants[J]. Solar Energy, 2016, 137: 236-245. [94] 王元章, 李智华, 吴春华. 一种四参数的光伏组件在线故障诊断方法[J]. 中国电机工程学报, 2014, 34(13): 2078-2087. Wang Yuanzhang, Li Zhihua, Wu Chunhua.A survey of online fault diagnosis for photovoltaic modules based on four parameters[J]. Proceedings of the CSEE, 2014, 34(13): 2078-2087. [95] 易威. 光伏阵列的参数辨识及故障诊断研究[D]. 杭州: 浙江大学, 2021. [96] 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. [97] 徐立娟, 吴春华, 王元章, 等. 基于决策树的光伏组件故障诊断方法研究[J]. 电工电能新技术, 2017, 36(6): 83-88. Xu Lijuan, Wu Chunhua, Wang Yuanzhang, et al.Survey of fault diagnosis for PV modules based on decision tree[J]. Advanced Technology of Electrical Engineering and Energy, 2017, 36(6): 83-88. [98] Yi Zhehan, Etemadi A H.Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine[J]. IEEE Transactions on Industrial Elec- tronics, 2017, 64(11): 8546-8556. [99] Karmacharya I M, Gokaraju R.Fault location in ungrounded photovoltaic system using wavelets and ANN[J]. IEEE Transactions on Power Delivery, 2018, 33(2): 549-559. [100] Akram M N, Lotfifard S.Modeling and health monitoring of DC side of photovoltaic array[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4): 1245-1253. [101] 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): 1864-1873. Chen Ling, Han Wei, Zhang Jingwei.PV module fault diagnosis based on data fusion[J]. Power System Technology, 2017, 41(6): 1864-1873. [102] 李光辉, 段晨东, 武珊. 基于半监督机器学习法的光伏阵列故障诊断[J]. 电网技术, 2020, 44(5): 1908-1913. Li Guanghui, Duan Chendong, Wu Shan.Fault diagnosis of PV array based on semi-supervised machine learning[J]. Power System Technology, 2020, 44(5): 1908-1913. [103] Zhao Ye, Ball R, Mosesian J, et al.Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays[J]. IEEE Transactions on Power Electronics, 2015, 30(5): 2848-2858. [104] Huang Junming, Wai R J, Yang Gengjie.Design of hybrid artificial bee colony algorithm and semi- supervised extreme learning machine for PV fault diagnoses by considering dust impact[J]. IEEE Transactions on Power Electronics, 2020, 35(7): 7086-7099.
2024, Vol. 39 
