[1] IRENA. World Energy Transitions Outlook 2022: 1.5℃ Pathway[Z]. Inter. Renewable Energy Agency Abu Dhabi, United Arab Emirates, 2022.
[2] Hasan K, Yousuf S B, Tushar M S H K, et al. Effects of different environmental and operational factors on the PV performance: A comprehensive review[J]. Energy Science & Engineering, 2022, 10(2): 656-675.
[3] Bansal N, Pany P, Singh G. Visual degradation and performance evaluation of utility scale solar photovoltaic power plant in hot and dry climate in western India[J]. Case Studies in Thermal Engineering, 2021, 26: 101010.
[4] Dhimish M. Thermal impact on the performance ratio of photovoltaic systems: A case study of 8000 photovoltaic installations[J]. Case Studies in Thermal Engineering, 2020, 21: 100693.
[5] Skomedal F, Aarseth B L, Haug H, et al. How much power is lost in a hot-spot? A case study quantifying the effect of thermal anomalies in two utility scale PV power plants[J]. Solar Energy, 2020, 211: 1255-1262.
[6] Akram M W, Li G, Jin Y, et al. Improved outdoor thermography and processing of infrared images for defect detection in PV modules[J]. Solar Energy, 2019, 190: 549-560.
[7] Satpathy P R, Sharma R. Parametric indicators for partial shading and fault prediction in photovoltaic arrays with various interconnection topologies[J]. Energy Conversion and Management, 2020, 219: 113018.
[8] Wang A, Xuan Y. Close examination of localized hot spots within photovoltaic modules[J]. Energy Conversion and Management, 2021, 234: 1B959.
[9] Kim C, Jeong M S, Ko J, et al. Inhomogeneous rear reflector induced hot-spot risk and power loss in building-integrated bifacial c-Si photovoltaic modules[J]. Renewable Energy, 2021, 163: 825-835.
[10] Guerriero P, Daliento S. Toward a hot spot free PV module[J]. IEEE J. of Photovoltaics, 2019, 9(3): 796-802.
[11] Manganiello P, Balato M, Vitelli M. A survey on mismatching and aging of PV modules: The closed loop[J]. IEEE Trans. on Industrial Electronics, 2015, 62(11): 7276-7286.
[12] Kntges M, Kurtz S, Packard C, et al. Review of failures of photovoltaic modules[Z]. IEA International Energy Agency, 2014.
[13] 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.
[14] Bauer J, Wagner J M, Lothyk A, et al. Hot spots in multicrystalline silicon solar cells: Avalanche breakdown due to etch pits[J]. Physica Status Solidi (RRL)-Rapid Research Letters, 2009, 3(2/3): 40-42.
[15] Akram M W, Li G, Jin Y, 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.
[16] Kim H, Xu D, John C, et al. Modeling thermo-mechanical stress of flexible CIGS solar cells[J]. IEEE J. of Photovoltaics, 2019, 9(2): 499-505.
[17] Wu Z, Hu Y, Wen J X, et al. A review for solar panel fire accident prevention in large-scale PV applications[J]. IEEE Access, 2020, 8: 132466-132480.
[18] Kim Y, Shim M, Lee M J, et al. Hot-spot generation model using electrical and thermal equivalent circuits for a copper indium gallium selenide photovoltaic module[J]. Solar Energy, 2021, 216: 377-385.
[19] Heliguy. Drones for solar panel inspections[EB/OL]. 2022-7-18[2023-5-22]. https://www.heliguy.com/blogs/posts/drones-for-solar-panel-inspections.
[21] Ali M U, Khan H F, Masud M, et al. A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography[J]. Solar Energy, 2020, 208: 643-651.
[22] Du B, He Y, He Y, et al. Intelligent classification of silicon photovoltaic cell defects based on eddy current thermography and convolution neural network[J]. IEEE Trans. on Industrial Informatics, 2020, 16(10): 6242-6251.
[23] Niazi K A K, Akhtar W, Khan H A, et al. Hotspot diagnosis for solar photovoltaic modules using a Naive Bayes classifier[J]. Solar Energy, 2019, 190: 34-43.
[25] Ali M U, Saleem S, Masood H, et al. Early hotspot detection in photovoltaic modules using color image descriptors: An infrared thermography study[J]. Inter. J. of Energy Research, 2021, 46(2): 774-785.
[27] Cipriani G, Damico A, Guarino S, et al. Convolutional neural network for dust and hotspot classification in PV modules[J]. Energies, 2020, 13(23): 6357.
[28] Nie J, Luo T, Li H. Automatic hotspots detection based on UAV infrared images for large-scale PV plant[J]. Electron. Lett., 2020, 56(19): 993-995.
[29] Manno D, Cipriani G, Ciulla G, et al. Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images[J]. Energy Conversion and Management, 2021, 241: 114315.
[30] Aikgz H, Korkmaz D, Dandil . Classification of hotspots in photovoltaic modules with deep learning methods[J]. Turkish J. of Science and Technol., 2022, 17(2): 211-221.
[34] Wei S, Li X, Ding S, et al. Hotspots infrared detection of photovoltaic modules based on hough line transformation and Faster-RCNN approach[C]// 6th Inter. Conf. on Control, Decision and Information Technologies (CoDIT), 2019: 1266-1271.
[40] Zheng Q, Ma J, Liu M, et al. Lightweight hot-spot fault detection model of photovoltaic panels in UAV remote-sensing image[J]. Sensors, 2022, 22(12): 4617.
[41] Sun T, Xing H, Cao S, et al. A novel detection method for hot spots of photovoltaic (PV) panels using improved anchors and prediction heads of YOLOv5 network[J]. Energy Reports, 2022, 8: 1219-1229.
[42] Su B, Chen H, Liu K, et al. RCAG-Net: Residual channelwise attention gate network for hot spot defect detection of photovoltaic farms[J]. IEEE Trans. on Instrumentation and Measurement, 2021, 70: 1-14.
[43] Ying Y, Qi Y, Rong L, et al. Anchor points based accurate fault locating in large-scale photovoltaic plants via aerial infrared videos[J]. IEEE J. of Photovoltaics, 2022, 12(1): 437-443.
[44] Carletti V, Greco A, Saggese A, et al. An intelligent flying system for automatic detection of faults in photovoltaic plants[J]. J. of Ambient Intelligence and Humanized Computing, 2019, 11(5): 2027-2040.
