Güneş Panellerinde Hücre Hasarının Tespiti ve Türleri: Kapsamlı Bir İnceleme

Year 2026, Volume: 17 Issue: 1, - , 23.03.2026

Ferdi Doğan , Miktat Aktaş , Nevzat Olgun , Mücahit Çalışan , İbrahim Türkoğlu

https://doi.org/10.24012/dumf.1704433

https://izlik.org/JA85NF62TF

Abstract

Güneş enerjisinden elektrik üretimi için kullanılan güneş enerjisi panellerinin sağlıklı çalışması, üretilen enerjinin verimliliği açısından önemlidir. Bu çalışmada güneş enerjisi panellerinden enerji üretimini etkileyen hasar türleri, bu hasarların enerji üretimine etkileri ve hasarların tespit yöntemleri ayrıntılı olarak ele alınmıştır. Güneş enerjisi panellerinde hasarlar çevresel, mekanik, termal ve elektriksel faktörlerden dolayı meydana gelmektedir. Bu hasarlar enerji üretimini doğrudan olumsuz yönde etkileyerek verimliliği düşürmektedir.
Çalışmada hasar türleri dört ana başlık altında incelenmiştir. Bunlar mekanik hasarlar (mikro çatlaklar, delaminasyon), termal hasarlar (sıcak nokta oluşumu, aşırı ısınma), elektriksel hasarlar (kısa devre, bağlantı kopmaları) ve çevresel hasarlardır (tozlanma, UV ışınlarının etkisi). Hasarların erken tespiti enerji kayıplarını önlemek ve panellerin ömrünü uzatmak için önemlidir. Hasar tespit yöntemleri incelendiğinde üç ana başlık altında toplanmıştır: Geleneksel yöntemler, ileri teknoloji tabanlı yöntemler ve diğer yöntemler.
Çalışmada elde edilen bulgular derlenerek hasar tespit süreçlerinde karşılaşılan zorluklar, geleneksel yöntemlerin yetersizlikleri ve yapay zekaya dayalı ileri teknolojik yöntemlerin avantaj ve dezavantajları ortaya konularak hasar tespit süreçlerine yönelik çözüm önerileri sunulmuştur. Bu çalışma hasar tespit yöntemlerinin geliştirilmesi ve uygulanması için önemli bir rehber niteliği taşımaktadır.

Keywords

Fotovoltaik paneller , erişilebilir ve temiz enerji , hasar tespit yöntemleri , yenilenebilir enerji , sürdürülebilir enerji

Project Number

123E697

Detection of Cell Damage and Types in Solar Panels: A Comprehensive Review

https://izlik.org/JA85NF62TF

Abstract

The healthy operation of solar energy panels used for electricity production from solar energy is important in terms of the efficiency of the energy produced. In this study, the types of damage that affect energy production from solar energy panels, the effects of these damages on energy production and the methods of detecting the damages are discussed in detail. Damages in solar energy panels occur due to environmental, mechanical, thermal and electrical factors. These damages directly affect energy production negatively and reduce efficiency.
The types of damages were examined under four main headings in the study. These are mechanical damages (micro cracks, delamination), thermal damages (hot spot formation, overheating), electrical damages (short circuit, disconnections) and environmental damages (dusting, effect of UV rays). Early detection of damages is important to prevent energy losses and extend the life of the panels. When the damage detection methods were examined, they were grouped under three main headings: Traditional methods, advanced technology-based methods and other methods.
The findings obtained in the study were compiled and the difficulties encountered in the damage detection processes, as well as the inadequacies of traditional methods and the advantages and disadvantages of advanced technological methods based on artificial intelligence were revealed, and solution suggestions for damage detection processes were presented. This study provides an important guide for the development and application of damage detection methods.

Keywords

Photovoltaic panels , accessible and clean energy , damage detection methods , renewable energy , sustainable energy

Supporting Institution

TUBİTAK 1001

Project Number

123E697

Thanks

The authors thank to TUBITAK for their supports.

References

• [1] G. Kopp and J. L. Lean, "A new, lower value of total solar irradiance: Evidence and climate significance," Geophysical Research Letters, vol. 38, no. 1, 2011.
• [2] I. Dincer, "Renewable energy and sustainable development: a crucial review," Renewable and sustainable energy reviews, vol. 4, no. 2, pp. 157-175, 2000.
• [3] N. S. M. N. Izam, Z. Itam, W. L. Sing, and A. Syamsir, "Sustainable development perspectives of solar energy technologies with focus on solar Photovoltaic—A review," Energies, vol. 15, no. 8, p. 2790, 2022.
• [4] F. H. Hasan, S. Algburi, and S. B. Ezzat, "Investigating the Impact of Internal and External Factors on Solar Cell Performance to Enhance Energy Conversion Efficiency," NTU Journal of Renewable Energy, vol. 8, no. 1, pp. 14-23, 2025.
• [5] H. H. Öztürk, "Güneş Enerjisinden Fotovoltaik Yöntemle Elektrik Üretiminde Güç Dönüşüm Verimi Ve Etkili Etmenler," 2008.
• [6] M. D. Patabo, J. G. Daud, S. I. Ponnokaraeng, G. Tongkotou, I. T. Kapoh, and Y. L. Sariowan, "Maintenance Of Solar Power Plants For Household Electricity Use," Jurnal Syntax Admiration, vol. 5, no. 12, pp. 5897-5903, 2024.
• [7] M. S. K. Mithil, M. A. Talha, and S. Ahmad, "Enhancing Solar System Efficiency Based on Precise Real-Time Energy Data Analysis and Sun Position Tracking," in 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 2024: IEEE, pp. 290-295.
• [8] L. El Chaar and N. El Zein, "Review of photovoltaic technologies," Renewable and sustainable energy reviews, vol. 15, no. 5, pp. 2165-2175, 2011.
• [9] R. B. Domurcuk, M. Asker, P. Demircioğlu, and İ. Böğrekci, "THE ANALYSIS OF PHOTOVOLTAIC PANEL SYSTEMS," International Journal of 3D Printing Technologies and Digital Industry, vol. 5, no. 1, pp. 13-22, 2021.
• [10] Y. Qian et al., "Thin-film organic semiconductor devices: from flexibility to ultraflexibility," Sci. China Mater, vol. 59, no. 7, pp. 589-608, 2016.
• [11] X. Li, P. Li, Z. Wu, D. Luo, H.-Y. Yu, and Z.-H. Lu, "Review and perspective of materials for flexible solar cells," Materials Reports: Energy, vol. 1, no. 1, p. 100001, 2021.
• [12] M. P. Maniscalco, S. Longo, G. Miccichè, M. Cellura, and M. Ferraro, "A critical review of the environmental performance of bifacial photovoltaic panels," Energies, vol. 17, no. 1, p. 226, 2023.
• [13] S. Sivaraj et al., "A comprehensive review on current performance, challenges and progress in thin-film solar cells," Energies, vol. 15, no. 22, p. 8688, 2022.
• [14] L. Wang et al., "Highly Efficient Monolithic Perovskite/TOPCon Silicon Tandem Solar Cells Enabled by “Halide Locking”," Advanced Materials, p. 2416150, 2025.
• [15] T. D. Lee and A. U. Ebong, "A review of thin film solar cell technologies and challenges," Renewable and Sustainable Energy Reviews, vol. 70, pp. 1286-1297, 2017.
• [16] M. Alves, A. Pérez-Rodríguez, P. J. Dale, C. Domínguez, and S. Sadewasser, "Thin-film micro-concentrator solar cells," Journal of Physics: Energy, vol. 2, no. 1, p. 012001, 2019.
• [17] R. A. Rahimi, S. H. Yahaya, D. F. M. H. Seria, and M. N. Sani, "A comprehensive review on architectural design and development of flexible photovoltaic solar panel," Multidisciplinary Reviews, vol. 7, no. 12, pp. 2024299-2024299, 2024.
• [18] R. O. Yakubu, L. D. Mensah, D. A. Quansah, and M. S. Adaramola, "A systematic literature review of the bifacial photovoltaic module and its applications," The Journal of Engineering, vol. 2024, no. 8, p. e12421, 2024.
• [19] X. Kong, T. He, H. Qiu, L. Zhan, and S. Yin, "Progress in organic photovoltaics based on green solvents: from solubility enhancement to morphology optimization," Chemical Communications, vol. 59, no. 81, pp. 12051-12064, 2023.
• [20] N. Jost, T. Gu, J. Hu, C. Domínguez, and I. Antón, "Integrated micro‐scale concentrating photovoltaics: a scalable path toward high‐efficiency, low‐cost solar power," Solar RRL, vol. 7, no. 16, p. 2300363, 2023.
• [21] O. O. Apeh, E. L. Meyer, and O. K. Overen, "Contributions of solar photovoltaic systems to environmental and socioeconomic aspects of national development—A review," Energies, vol. 15, no. 16, p. 5963, 2022.
• [22] H. Huq, "Solarenergy Fuels for Sustainable Livelihoods: Case Study of Southwest Coastal Region of Bangladesh," Geography, Environment, Sustainability, vol. 11, no. 4, pp. 132-143, 2019.
• [23] M. Amani, A. Smaili, and A. Ghenaiet, "Thermo-economic assessment of the first integrated solar combined cycle system of hassi r’mel," Mechanics, vol. 26, no. 3, pp. 242-251, 2020.
• [24] L. Kulikova, A. Goshunova, and D. Nutfullina, "Economic Analysis of Solar Energy Using in Oil Sector Economy in Republic of Tatarstan," in IOP Conference Series: Materials Science and Engineering, 2017, vol. 262, no. 1: IOP Publishing, p. 012066.
• [25] M. M. Rahman, A. Islam, S. Salehin, and M. A. Al-Matin, "Development of a model for techno-economic assessment of a stand-alone off-grid solar photovoltaic system in Bangladesh," Int. J. Renew. Energy Res, vol. 6, no. 1, pp. 140-149, 2016.
• [26] T. Rus, R.-P. Moldovan, and M. Á. Pardo Picazo, "LCA analysis of a roof mounted PV system: a Romanian case study," Frontiers in Environmental Science, vol. 12, p. 1413629, 2024.
• [27] N. A. Mostafa and A. Aboelezz, "Feasibility-sustainability study of power generation using solar energy at an industrial site: a case study from Egypt," Energy, Sustainability and Society, vol. 14, no. 1, p. 36, 2024.
• [28] P. Ponce, C. Pérez, A. R. Fayek, and A. Molina, "Solar energy implementation in manufacturing industry using multi-criteria decision-making fuzzy TOPSIS and S4 framework," Energies, vol. 15, no. 23, p. 8838, 2022.
• [29] Y. Gebreyohannes, M. Bayray, and J. Lauwaert, "A review on solar thermal utilization for industrial heating and cooling processes: global and Ethiopian perspective," Momona Ethiopian Journal of Science, vol. 12, no. 2, pp. 232-256, 2020.
• [30] V. Brezoczki and G. Filip, "Aspects of the use solar energy valorification for industrial and public lighten area," in IOP Conference Series: Materials Science and Engineering, 2020, vol. 749, no. 1: IOP Publishing, p. 012028.
• [31] J. Hyvönen, A. Santasalo-Aarnio, S. Syri, and M. Lehtonen, "Feasibility study of energy storage options for photovoltaic electricity generation in detached houses in Nordic climates," Journal of Energy Storage, vol. 54, p. 105330, 2022.
• [32] A. Vasić-Milovanović, "Increased temperature and radiation damage influence on solar cells characteristics," FME transactions, vol. 41, no. 1, pp. 77-81, 2013.
• [33] S. Duman and M. Alçı, "Yarım ve tam fotovoltaik hücreleri ile tasarlanan güneş enerjisi panellerinin toplam verimliliğini etkileyen parametrelerinin incelenmesi," Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 11, no. 3, pp. 592-600, 2022.
• [34] D. Dwivedi, P. K. Yemula, and M. Pal, "Detection of malfunctioning modules in photovoltaic power plants using unsupervised feature clustering segmentation algorithm," arXiv preprint arXiv:2212.14653, 2022.
• [35] M. Dhimish, V. d'Alessandro, and S. Daliento, "Investigating the impact of cracks on solar cells performance: Analysis based on nonuniform and uniform crack distributions," IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 1684-1693, 2021.
• [36] I. Bodnár, D. Matusz-Kalász, and R. R. Boros, "Exploration of Solar Panel Damage and Service Life Reduction Using Condition Assessment, Dust Accumulation, and Material Testing," Sustainability, vol. 15, no. 12, p. 9615, 2023.
• [37] F. Doğan, S. Oyucu, E. Bicer, and A. Aksoz, "Deep learning models for damage type detection in wind turbines," PeerJ Computer Science, vol. 11, p. e3163, 2025.
• [38] M. Bdour, Z. Dalala, M. Al-Addous, A. Radaideh, and A. Al-Sadi, "A comprehensive evaluation on types of microcracks and possible effects on power degradation in photovoltaic solar panels," Sustainability, vol. 12, no. 16, p. 6416, 2020.
• [39] M. Aktaş, F. Doğan, and İ. Türkoğlu, "Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models," Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 2, pp. 217-226, 2024.
• [40] M. Dhimish, V. Holmes, M. Dales, and B. Mehrdadi, "Effect of micro cracks on photovoltaic output power: case study based on real time long term data measurements," Micro & Nano Letters, vol. 12, no. 10, pp. 803-807, 2017.
• [41] T. Rahman et al., "Investigation of degradation of solar photovoltaics: A review of aging factors, impacts, and future directions toward sustainable energy management," Energies, vol. 16, no. 9, p. 3706, 2023.
• [42] A. A. Hasan, A. Ahmed Alkahtani, S. A. Shahahmadi, M. Nur E. Alam, M. A. Islam, and N. Amin, "Delamination-and electromigration-related failures in solar panels—A review," Sustainability, vol. 13, no. 12, p. 6882, 2021.
• [43] G. Oreski and G. Pinter, "Peeling of Flexible Laminates—Determination of Interlayer Adhesion of Backsheet Laminates Used for Photovoltaic Modules," Materials, vol. 15, no. 9, p. 3294, 2022.
• [44] R. Herrero et al., "Experimental analysis and simulation of a production line for CPV modules: impact of defects, misalignments, and binning of receivers," Energy Science & Engineering, vol. 5, no. 5, pp. 257-269, 2017.
• [45] F. Spertino and J. S. Akilimali, "Are manufacturing $ I $–$ V $ mismatch and reverse currents key factors in large photovoltaic arrays?," IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4520-4531, 2009.
• [46] M. Dhimish and G. Badran, "Investigating defects and annual degradation in UK solar PV installations through thermographic and electroluminescent surveys," npj Materials Degradation, vol. 7, no. 1, p. 14, 2023.
• [47] X. Chen, T. Karin, and A. Jain, "Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques," Applied Energy, vol. 377, p. 124462, 2025.
• [48] Y. Aoki, M. Okamoto, A. Masuda, and T. Doi, "Module performance degradation with rapid thermal-cycling," Proceedings of Renewable Energy, 2010.
• [49] C. Han, "Simulation of series resistance increase through solder layer cracking in Si solar cells under thermal cycling," Energies, vol. 16, no. 6, p. 2524, 2023.
• [50] O. E. Ikejiofor, Y. E. Asuamah, H. O. Njoku, and S. O. Enibe, "Detection of hotspots and performance deteriotations in pv modules under partial shading conditions using infrared thermography," Engineering Proceedings, vol. 2, no. 1, p. 71, 2020.
• [51] J. Zaraket, N. Kokanyan, M. Aillerie, and C. Salame, "Evolution of PV solar modules parameters operating in extreme environments," in AIP Conference Proceedings, 2020, vol. 2307, no. 1: AIP Publishing.
• [52] D. Razia, A. Raj, S. P. Singh, A. Amudha, P. Karthigaikumar, and N. Wasatkar, "Electrical Deterioration Caused by Lightning: Implications for Solar Power Plants," in 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC), 2023: IEEE, pp. 612-615.
• [53] A. Arjhangmehr and S. A. H. Feghhi, "Displacement damage analysis and modified electrical equivalent circuit for electron and photon-irradiated silicon solar cells," Radiation Effects and Defects in Solids, vol. 169, no. 10, pp. 874-884, 2014.
• [54] C. M. Whitaker, B. G. Pierce, A. M. Karimi, R. H. French, and J. L. Braid, "PV cell cracks and impacts on electrical performance," in 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), 2020: IEEE, pp. 1417-1422.
• [55] N. G. Irias, H. de Paula, P. Pereira, E. N. Cardoso, and B. M. Lopes, "Degradation of photovoltaic panels induced by electric potential: Theoretical survey and computational study on the inverter operation influence," in 2015 IEEE 13th Brazilian Power Electronics Conference and 1st Southern Power Electronics Conference (COBEP/SPEC), 2015: IEEE, pp. 1-6.
• [56] Y. Zhang, Z. Lv, M. Yu, J. Tang, and W. Wei, "Fault tolerance enhancement of the PV module system by improving the topology and control strategy," IET Generation, Transmission & Distribution, vol. 14, no. 6, pp. 975-985, 2020.
• [57] W. G. Shin, S. W. Ko, H. J. Song, Y. C. Ju, H. M. Hwang, and G. H. Kang, "Origin of bypass diode fault in c-Si photovoltaic modules: Leakage current under high surrounding temperature," Energies, vol. 11, no. 9, p. 2416, 2018.
• [58] J. Johnson, "Electrical and Thermal Finite Element Modeling of Arc Faults in Photovoltaic Bypass Diodes," Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA …, 2012.
• [59] R. Pomponi and R. Tommasini, "Risk assessment and lightning protection for PV systems and solar power plants," RE&PQJ, vol. 10, no. 9, 2012.
• [60] N. H. Zaini et al., "Lightning surge analysis on a large scale grid-connected solar photovoltaic system," Energies, vol. 10, no. 12, p. 2149, 2017.
• [61] A. Formisano, C. Petrarca, J. C. Hernández, and F. J. Muñoz‐Rodríguez, "Assessment of induced voltages in common and differential‐mode for a PV module due to nearby lightning strikes," IET Renewable Power Generation, vol. 13, no. 8, pp. 1369-1378, 2019.
• [62] N. Pinochet, R. Couderc, and S. Therias, "Solar cell UV‐induced degradation or module discolouration: Between the devil and the deep yellow sea," Progress in Photovoltaics: Research and Applications, vol. 31, no. 11, pp. 1091-1100, 2023.
• [63] J. Ciempka, A. Thomson, and I. Haedrich, "Impact of Damp Heat and Ultraviolet Radiation on Common Solar Module Encapsulant Materials," 2015.
• [64] A. Sinha et al., "UV‐induced degradation of high‐efficiency silicon PV modules with different cell architectures," Progress in Photovoltaics: Research and Applications, vol. 31, no. 1, pp. 36-51, 2023.
• [65] B. Mohamed, S. Zambou, and S. S. Zekeng, "Influence of moisture on the operation of a mono-crystalline based silicon photovoltaic cell: A numerical study using SCAPS 1 D," arXiv preprint arXiv:1712.08117, 2017.
• [66] H. H. Al-Kayiem and M. N. Reda, "Analysis of solar photovoltaic panel integrated with ground heat exchanger for thermal management," International Journal of Energy Production and Management, vol. 6, no. 1, pp. 17-31, 2021.
• [67] A. Pagodaripour, A. Ghasemkhani, H. Ghazizade-Ahsaee, and A. Namjo, "The assessment and experimental study of photovoltaics panel by spraying water (case study: Kerman, Iran)," Energy Equipment and Systems, vol. 8, no. 4, pp. 389-399, 2020.
• [68] S. S. Jaafar, H. A. Maarof, R. T. Salh, H. Sahib, and Y. H. Azeez, "Non-Uniform Dust Distribution Effect On Photovoltaic Panel Performance," Renewable Energy & Sustainable Development, vol. 9, no. 1, 2023.
• [69] M. Katoch, V. Dahiya, and S. K. Yadav, "The performance analysis of dusty photovoltaic panel," Archives of Thermodynamics, vol. 44, no. 2, 2023.
• [70] R. Pimpalkar, A. Sahu, and R. B. Patil, "An Artificial Intelligence and Machine Learning Model to Estimate the Cleaning Periodicity for Dusty Solar Photovoltaic (PV) Modules in A Composite Environment," Journal of Mines, Metals and Fuels, pp. 2794-2804, 2023.
• [71] A. M. Aly, G. Bksuamlak, and V. Crepel, "Wind loads on ground-mounted solar panels: a multi-scale computational and experimental study," 2013.
• [72] H. Alrawashdeh and T. Stathopoulos, "Experimental investigation of the wind loading on solar panels: effects of clearance off flat roofs," Journal of Structural Engineering, vol. 148, no. 12, p. 04022202, 2022.
• [73] T. Yambe, Y. Uematsu, and K. Sato, "Wind loads on roofing system and photovoltaic system installed parallel to flat roof," Proceedings International Structure Engineering Constraction, vol. 7, no. 1, 2020.
• [74] M. Aghaei et al., "Review of degradation and failure phenomena in photovoltaic modules," Renewable and Sustainable Energy Reviews, vol. 159, p. 112160, 2022.
• [75] S. Jin, Y. Liu, and C. He, "Detection method of solar cell surface defects based on C4. 5-G algorithm," in International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, vol. 12712: SPIE, pp. 184-191.
• [76] K. Nakagawa and G. Fujita, "Comparative analysis of methods for determining degradation of PV modules and image analysis: Evaluation of Efficient Inspection Techniques Using Thermal Infrared Imaging Electroluminescence Imaging Techniques," in 2024 6th International Conference on Power Engineering and Renewable Energy (ICPERE), 2024: IEEE, pp. 1-4.
• [77] Z. Xi, Z. Lou, Y. Sun, X. Li, Q. Yang, and W. Yan, "A vision-based inspection strategy for large-scale photovoltaic farms using an autonomous UAV," in 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 2018: IEEE, pp. 200-203.
• [78] A. Shihavuddin et al., "Image based surface damage detection of renewable energy installations using a unified deep learning approach," Energy Reports, vol. 7, pp. 4566-4576, 2021.
• [79] A. M. Gabor et al., "The impact of cracked solar cells on solar panel energy delivery," in 2020 47th IEEE photovoltaic specialists conference (PVSC), 2020: IEEE, pp. 0810-0813.
• [80] R. Smith and D. Colvin, "Low-Current Diagnostic Metric for Photovoltaic Module Damage," in 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC), 2024: IEEE, pp. 0189-0189.
• [81] I. Khan and S. Khan, "Thermal imaging based maximum power point tracking for solar modules in variable ambient temperature," SN Applied Sciences, vol. 3, no. 5, p. 536, 2021.
• [82] F. J. Suárez-Domínguez, M. B. Prendes-Gero, Á. Martín-Rodríguez, and A. Higuera-Garrido, "IR thermography applies to the detection of solar panel," Revista de la Construcción, vol. 14, no. 3, pp. 9-14, 2015.
• [83] R. Vaillon, O. Dupré, R. B. Cal, and M. Calaf, "Pathways for mitigating thermal losses in solar photovoltaics," Scientific reports, vol. 8, no. 1, p. 13163, 2018.
• [84] M. Zegrar, M. T. Benmessaoud, and F. Z. Zerhouni, "Design and implementation of an IV curvetracer dedicated to characterize PV panels," International Journal of Electrical and Computer Engineering, vol. 11, no. 3, p. 2011, 2021.
• [85] M. García et al., "Outdoor IV characterization of tilted and vertical bifacial PV modules," in Journal of Physics: Conference Series, 2023, vol. 2538, no. 1: IOP Publishing, p. 012002.
• [86] A. Augusto, A. Killam, S. G. Bowden, and H. Wilterdink, "Measuring outdoor I–V characteristics of PV modules and systems," Progress in Energy, vol. 4, no. 4, p. 042006, 2022.
• [87] R. Araneo and M. Mitolo, "Insulation resistance and failures of a high-power grid-connected photovoltaic installation: A case study," IEEE Industry Applications Magazine, vol. 27, no. 3, pp. 16-22, 2021.
• [88] N. Ketjoy, P. Mensin, and W. Chamsa-Ard, "Impacts on insulation resistance of thin film modules: A case study of a flooding of a photovoltaic power plant in Thailand," PLoS One, vol. 17, no. 9, p. e0274839, 2022.
• [89] A. Thakfan and Y. Bin Salamah, "Artificial-intelligence-based detection of defects and faults in photovoltaic systems: A survey," Energies, vol. 17, no. 19, p. 4807, 2024.
• [90] Z. B. Duranay, "Fault detection in solar energy systems: A deep learning approach," Electronics, vol. 12, no. 21, p. 4397, 2023.
• [91] S. Jaybhaye, V. Sirvi, S. Srivastava, V. Loya, V. Gujarathi, and M. Jaybhaye, "Classification and Early Detection of Solar Panel Faults with Deep Neural Network Using Aerial and Electroluminescence Images," Journal of Failure Analysis and Prevention, vol. 24, no. 4, pp. 1746-1758, 2024.
• [92] G. Tanda and M. Migliazzi, "Infrared thermography monitoring of solar photovoltaic systems: A comparison between UAV and aircraft remote sensing platforms," Thermal Science and Engineering Progress, vol. 48, p. 102379, 2024.
• [93] D. F. Ramirez, D. Pujara, C. Tepedelenlioglu, D. Srinivasan, and A. Spanias, "Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection," in 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA), 2024: IEEE, pp. 1-4.
• [94] Y. Hong, R. Pan, J. Su, and M. Li, "Infrared image detection of defects in lightweight solar panels based on improved MSRCR and YOLOv8n," Infrared Physics & Technology, vol. 141, p. 105473, 2024.
• [95] C. Ruan, W. Tang, X. Hu, and W. Yan, "Deep learning-based method for PV panels segmentation and defects detection with infrared images," in 2021 China Automation Congress (CAC), 2021: IEEE, pp. 7166-7171.
• [96] R. Duan and Z. Ma, "A method for detecting photovoltaic panel faults using a drone equipped with a multispectral camera," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 10, pp. 59-65, 2024.
• [97] Z. Wang, P. Zheng, B. Bahadir Kocer, and M. Kovac, "Drone‐Based Solar Cell Inspection With Autonomous Deep Learning," Infrastructure Robotics: Methodologies, Robotic Systems and Applications, pp. 337-365, 2024.
• [98] F. Doğan and İ. Türkoğlu, "Derin öğrenme algoritmalarının yaprak sınıflandırma başarımlarının karşılaştırılması," Sakarya University Journal of Computer and Information Sciences, vol. 1, no. 1, pp. 10-21, 2018.
• [99] F. Doğan and İ. Türkoğlu, "Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme," Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 10, no. 2, pp. 409-445, 2019.
• [100] M. Aktas, F. Doğan, and İ. Türkoğlu, "Analysis Of Cracks In Photovoltaic Module Cells From Electroluminescence Images By Deep Learning," in 1st International Conference on Computing and Machine Intelligence, 2021, pp. 103-103.
• [101] S. Jaybhaye, O. Thakur, R. Yardi, V. Raut, and A. Raut, "Solar panel damage detection and localization of thermal images," Journal of Failure Analysis and Prevention, vol. 23, no. 5, pp. 1980-1990, 2023.
• [102] T. Takashima, J. Yamaguchi, K. Otani, K. Kato, and M. Ishida, "Experimental studies of failure detection methods in PV module strings," in 2006 IEEE 4th World Conference on Photovoltaic Energy Conference, 2006, vol. 2: IEEE, pp. 2227-2230.
• [103] T. Takashima, J. Yamaguchi, and M. Ishida, "Fault detection by signal response in PV module strings," in 2008 33rd IEEE Photovoltaic Specialists Conference, 2008: IEEE, pp. 1-5.
• [104] Z. Li, K. Wu, Z. Yang, C. Wu, and F. Wang, "Fault Diagnosis of Photovoltaic Array Based on BP Neural Network and SSTDR," in 2023 IEEE PELS Students and Young Professionals Symposium (SYPS), 2023: IEEE, pp. 1-6.
• [105] M. U. Saleh et al., "Detection and localization of damaged photovoltaic cells and modules using spread spectrum time domain reflectometry," IEEE Journal of Photovoltaics, vol. 11, no. 1, pp. 195-201, 2020.
• [106] T. Pei and X. Hao, "A fault detection method for photovoltaic systems based on voltage and current observation and evaluation," Energies, vol. 12, no. 9, p. 1712, 2019.
• [107] Y. Tao, T. Yu, and J. Yang, "Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning," Sensors, vol. 25, no. 1, p. 136, 2024.
• [108] M. A. Raslan and Ç. Ertuğrul, "Fault Detection and Diagnosis Technic Using Electrical Characteristics of a PV Module and Machine Learning Classifier," Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, vol. 12, no. 3, pp. 73-88, 2020.
• [109] B. K. Karmakar and A. K. Pradhan, "Detection and classification of faults in solar PV array using Thevenin equivalent resistance," IEEE Journal of Photovoltaics, vol. 10, no. 2, pp. 644-654, 2020.
• [110] W. Wang, A. C.-F. Liu, H. S.-H. Chung, R. W.-H. Lau, J. Zhang, and A. W.-L. Lo, "Fault diagnosis of photovoltaic panels using dynamic current–voltage characteristics," IEEE Transactions on Power Electronics, vol. 31, no. 2, pp. 1588-1599, 2015.
• [111] W. Wang, A. C.-f. Liu, H. S.-h. Chung, R. W.-h. Lau, J. Zhang, and A. W.-l. Lo, "Fault diagnostic device for photovoltaic panels," in 2015 IEEE Applied Power Electronics Conference and Exposition (APEC), 2015: IEEE, pp. 2609-2616.
• [112] J. C. Treece and B. F. Shamee, "Detecting Cracks in Semiconductor Solarcells from Eddy-Current Measurements," in Review of Progress in Quantitative Nondestructive Evaluation: Volume 8, Part A and B: Springer, 1989, pp. 1281-1288.
• [113] K. Honda, K. Aikawa, and M. Uno, "Electrical Diagnosis Technique Using Differential Power Processing Converters for Photovoltaic Panels," in IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020: IEEE, pp. 1791-1796.
• [114] J. Nyakuchena, X. Zhang, and J. Huang, "Synchrotron based transient x-ray absorption spectroscopy for emerging solid-state energy materials," Chemical Physics Reviews, vol. 4, no. 2, 2023.
• [115] J. Aulich et al., "Spectrometric Characterization for Triple‐Junction Solar Cells," Solar RRL, vol. 8, no. 3, p. 2300783, 2024.
• [116] M. De Biasio, J. Zikulnig, W. Muehleisen, M. Simor, and P. J. Bolt, "Raman spectroscopy and photoluminescence imaging for predictive quality monitoring of CIGS solar cells," in Dimensional Optical Metrology and Inspection for Practical Applications XI, 2022, vol. 12098: SPIE, pp. 137-143.
• [117] L. Gedvilas et al., "FTIR Laboratory in Support of the PV Program," National Renewable Energy Lab.(NREL), Golden, CO (United States), 2003.
• [118] J. P. Ganesan et al., "Raman microspectroscopy of a silicon solar cell," in 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), 2021: IEEE, pp. 1014-1017.
• [119] G. C. Eder, Y. Lin, Y. Voronko, and L. Spoljaric-Lukacic, "On-site identification of the material composition of PV modules with mobile spectroscopic devices," Energies, vol. 13, no. 8, p. 1903, 2020.
• [120] K. Ali, A. A. Abdallah, M. Kivambe, J. H. Zaini, and M. M. Nauman, "Visual and IR Inspection Analysis of PV Modules Installed at the Desert Climate of Qatar," in Defect and Diffusion Forum, 2023, vol. 428: Trans Tech Publ, pp. 149-154.
• [121] S. R. Joshua, S. Park, and K. Kwon, "Visual Diagnostics: Deep Learning with DenseNet121 for Identifying Faults in Solar Panels," in 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), 2024: IEEE, pp. 1514-1519.
• [122] F. Doğan, İ. Türkoğlu, "Comparison of deep learning models in terms of multiple object detection on satellite images," Journal of Engineering Research, vol. 10, no. 3, pp. 89-108, 2021.
• [123] E. Kaplani, "PV cell and module degradation, detection and diagnostics," in Renewable Energy in the Service of Mankind Vol II: Selected Topics from the World Renewable Energy Congress WREC 2014, 2016: Springer, pp. 393-402.
• [124] L. Cardinale-Villalobos, C. Meza, A. Méndez-Porras, and L. D. Murillo-Soto, "Quantitative comparison of infrared thermography, visual inspection, and electrical analysis techniques on photovoltaic modules: a case study," Energies, vol. 15, no. 5, p. 1841, 2022.
• [125] L. Cardinale-Villalobos, R. Rimolo-Donadio, and C. Meza, "Solar panel failure detection by infrared UAS digital photogrammetry: a case study," Int. J. Renew. Energy Res.(IJRER), vol. 10, no. 3, pp. 1154-1164, 2020.
• [126] C. E. Packard, J. H. Wohlgemuth, and S. R. Kurtz, "Development of a visual inspection data collection tool for evaluation of fielded PV module condition," National Renewable Energy Lab.(NREL), Golden, CO (United States), 2012.
• [127] M. Chicca and G. TamizhMani, "Nondestructive techniques to determine degradation modes: Experimentation with 18 years old photovoltaic modules," in 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), 2015: IEEE, pp. 1-5.
• [128] S. Roy, S. K. Gupta, J. Prakash, G. Habib, K. Baudh, and M. Nasr, "Ecological and human health risk assessment of heavy metal contamination in road dust in the National Capital Territory (NCT) of Delhi, India," Environmental Science and Pollution Research, vol. 26, pp. 30413-30425, 2019.
• [129] S. Prabhakaran, R. A. Uthra, and J. Preetharoselyn, "Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels," Computer Systems Science & Engineering, vol. 44, no. 3, 2023.
• [130] A. Haeruman, S. U. Haq, M. Mohandes, S. Rehman, and S. S. I. Mitu, "AI-Based PV Panels Inspection using an Advanced YOLO Algorithm," Materials Research Proceedings, vol. 43.
• [131] A. Barrett, D. Bratanov, N. Amarasingam, D. Sera, and F. Gonzalez, "Machine Learning Based Damage Detection in Photovoltaic Arrays Using UAV-Acquired Infrared and Visual Imagery," in 2024 International Conference on Unmanned Aircraft Systems (ICUAS), 2024: IEEE, pp. 264-271.
• [132] C. Buerhop‐Lutz, O. Stroyuk, O. Mashkov, J. A. Hauch, and I. M. Peters, "Unveiling the Potential of Ultraviolet Fluorescence Imaging as a Versatile Inspection Tool: Insights from Extensive Photovoltaic Module Inspections in Multi‐MWp Photovoltaic Power Stations," Solar RRL, vol. 8, no. 24, p. 2400566, 2024.
• [133] R. Ebner, B. Kubicek, and G. Ujvari, "Non-destructive techniques for quality control of PV modules: Infrared thermography, electro-and photoluminescence imaging," in IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society, 2013: IEEE, pp. 8104-8109.
• [134] R. Bhoopathy, O. Kunz, M. Juhl, T. Trupke, and Z. Hameiri, "Outdoor photoluminescence imaging of photovoltaic modules with sunlight excitation," Progress in Photovoltaics: Research and Applications, vol. 26, no. 1, pp. 69-73, 2018.
• [135] B. Doll et al., "Aerial photoluminescence imaging of photovoltaic modules," physica status solidi (RRL)–Rapid Research Letters, vol. 17, no. 12, p. 2300059, 2023.
• [136] B. Doll et al., "Photoluminescence for defect detection on full-sized photovoltaic modules," IEEE Journal of Photovoltaics, vol. 11, no. 6, pp. 1419-1429, 2021.
• [137] J. Zikulnig, W. Mühleisen, P. J. Bolt, M. Simor, and M. De Biasio, "Photoluminescence imaging for the in-line quality control of thin-film solar cells," in Solar, 2022, vol. 2, no. 1: MDPI, pp. 1-11.
• [138] A. Redondo Plaza et al., "Partial photoluminescence imaging for inspection of photovoltaic cells: Artificial LED excitation and sunlight excitation," Energies, vol. 16, no. 11, p. 4531, 2023.
• [139] B. True, "Time resolved photoluminescence imaging systems and methods for photovoltaic cell inspection," ed: Google Patents, 2011.
• [140] M. Dong, Y. Qian, H. Wang, and Y. Lang, "Photoluminescence detection method for silicon photovoltaic modules at high light level," Optical Engineering, vol. 62, no. 3, pp. 033102-033102, 2023.
• [141] M. Abdelhamid, R. Singh, and M. Omar, "Review of microcrack detection techniques for silicon solar cells," IEEE Journal of Photovoltaics, vol. 4, no. 1, pp. 514-524, 2013.
• [142] K. G. Bedrich, "Quantitative electroluminescence measurements of PV devices," Loughborough University, 2017.
• [143] I. Polymeropoulos, S. Bezyrgiannidis, E. Vrochidou, and G. A. Papakostas, "Enhancing solar plant efficiency: A review of vision-based monitoring and fault detection techniques," Technologies, vol. 12, no. 10, p. 175, 2024.
• [144] I. Høiaas, K. Grujic, A. G. Imenes, I. Burud, E. Olsen, and N. Belbachir, "Inspection and condition monitoring of large-scale photovoltaic power plants: A review of imaging technologies," Renewable and Sustainable Energy Reviews, vol. 161, p. 112353, 2022.
• [145] A. Cardoso, D. Jurado-Rodríguez, A. López, M. I. Ramos, and J. M. Jurado, "Automated detection and tracking of photovoltaic modules from 3D remote sensing data," Applied Energy, vol. 367, p. 123242, 2024.
• [146] X. Zhang et al., "Inspection and classification system of photovoltaic module defects based on UAV and thermal imaging," in 2022 7th International Conference on Power and Renewable Energy (ICPRE), 2022: IEEE, pp. 905-909.
• [147] N. Le, H. Vu, N. Porntipworawech, S. Waisayarat, and M. Doan, "Integration of Aerial Thermal Imaging and Deep Learning for Fault Detection in Photovoltaic Panels: A Study at Thinh Long Solar Power Plant," in 2024 International Conference on Smart Energy Systems and Technologies (SEST), 2024: IEEE, pp. 1-6.
• [148] D. Rocha et al., "Multidefect detection tool for large-scale PV plants: Segmentation and classification," IEEE Journal of Photovoltaics, vol. 13, no. 2, pp. 291-295, 2023.
• [149] G. Terzoglou, M. Loufakis, P. Symeonidis, D. Ioannidis, and D. Tzovaras, "Employing deep learning framework for improving solar panel defects using drone imagery," in 2023 24th International Conference on Digital Signal Processing (DSP), 2023: IEEE, pp. 1-5.
• [150] B. Urtasun et al., "Pulsed thermography digital motion stabilization for the unmanned vehicle inspection of solar farms and gfrp wind blades through uavs and ugvs," in Thermosense: Thermal Infrared Applications XLIII, 2021, vol. 11743: SPIE, pp. 42-57.
• [151] O. I. Olayiwola and F. Camara, "Challenges and opportunities for autonomous UAV inspection in solar photovoltaics," in International Conference on Renewable Energy and Environment Engineering, 2024: EDP Sciences, p. 01003.
• [152] P. Kuznetsov et al., "Method for the automated inspection of the surfaces of photovoltaic modules," Sustainability, vol. 14, no. 19, p. 11930, 2022.
• [153] P. Moorthy et al., "Aerial Intelligence for Precision Monitoring and Fault Identification in Large-Scale Solar Farms Using Advanced Drone Technology and Deep Learning Technique," in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024: IEEE, pp. 1-7.
• [154] Ö. Baltacı, Z. Kıral, K. Dalkılınç, and O. Karaman, "Thermal image and inverter data analysis for fault detection and diagnosis of PV systems," Applied Sciences, vol. 14, no. 9, p. 3671, 2024.
• [155] S. Gallardo-Saavedra, L. Hernádez-Callejo, and Ó. Duque-Pérez, "Analysis and characterization of PV module defects by thermographic inspection," Revista Facultad de Ingeniería Universidad de Antioquia, no. 93, pp. 92-104, 2019.
• [156] W. Ahmed, M. U. Ali, M. P. Mahmud, K. A. K. Niazi, A. Zafar, and T. Kerekes, "A comparison and introduction of novel solar panel’s fault diagnosis technique using deep-features shallow-classifier through infrared thermography," Energies, vol. 16, no. 3, p. 1043, 2023.
• [157] F. H. K. Al-Zaabi, A. M. S. Al Washahi, R. K. M. Al-Maaini, and M. K. Boddu, "Advancing Solar PV Component Inspection: Early Defect Detection with UAV Based Thermal Imaging and Machine Learning," in 2023 Middle East and North Africa Solar Conference (MENA-SC), 2023: IEEE, pp. 1-3.
• [158] D. Kim, J. Youn, and C. Kim, "Automatic fault recognition of photovoltaic modules based on statistical analysis of UAV thermography," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 179-182, 2017.
• [159] A. El-Amiri, A. Saifi, A. Obbadi, Y. Errami, S. Sahnoun, and A. Elhassnaoui, "Defects detection in bi-facial photovoltaic modules PV using pulsed thermography," in 2018 Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE), 2018: IEEE, pp. 1-6.
• [160] G. Acciani, G. Simione, and S. Vergura, "Thermographic analysis of photovoltaic panels," in International Conference on Renewable Energies and Power Quality (ICREPQ’10), 2010, pp. 23-25.
• [161] M. Dávila-Sacoto, L. Hernández-Callejo, V. Alonso-Gómez, S. Gallardo-Saavedra, and L. G. González, "Detecting hot spots in photovoltaic panels using low-cost thermal cameras," in Ibero-American Congress of Smart Cities, 2019: Springer, pp. 38-53.
• [162] O. Menéndez, R. Guamán, M. Pérez, and F. Auat Cheein, "Photovoltaic modules diagnosis using artificial vision techniques for artifact minimization," Energies, vol. 11, no. 7, p. 1688, 2018.
• [163] P. Guerriero, G. Cuozzo, and S. Daliento, "Health diagnostics of PV panels by means of single cell analysis of thermographic images," in 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), 2016: IEEE, pp. 1-6.
• [164] L. Bommes, T. Pickel, C. Buerhop‐Lutz, J. Hauch, C. Brabec, and I. M. Peters, "Computer vision tool for detection, mapping, and fault classification of photovoltaics modules in aerial IR videos," Progress in Photovoltaics: Research and Applications, vol. 29, no. 12, pp. 1236-1251, 2021.
• [165] K. Nakagawa, A. Kaligambe, and G. Fujita, "Comparative Analysis of Defective Solar PV Module Inspection Using Thermal Infrared and Electroluminescence Imaging Techniques," in 2023 4th International Conference on High Voltage Engineering and Power Systems (ICHVEPS), 2023: IEEE, pp. 98-102.
• [166] T. Olšan, M. Libra, V. Poulek, B. Chalupa, and J. Sedláček, "Combination of three methods of photovoltaic panels damage evaluation," Sci Agric Bohem, vol. 48, no. 2, pp. 98-101, 2017.
• [167] A. Eskandari, A. Nedaei, J. Milimonfared, and M. Aghaei, "A multilayer integrative approach for diagnosis, classification and severity detection of electrical faults in photovoltaic arrays," Expert Systems with Applications, vol. 252, p. 124111, 2024.
• [168] M. Abdelsattar Mohamed Saeed, A. A. A. Rasslan, and A. Emad-Eldeen, "Comparative Analysis of Machine Learning Techniques for Fault Detection in Solar Panel Systems," SVU-International Journal of Engineering Sciences and Applications, vol. 5, no. 2, pp. 140-152, 2024.
• [169] H. S. Muttashar and A. M. Shakir, "Enhancing PV Fault Detection Using Machine Learning: Insights from a Simulated PV System," 2024.
• [170] A. Hadrawi, Y. S. Akil, and D. Utamidewi, "A Deep Autoencoder-Based Method for Detecting Mismatch Faults in Photovoltaic Modules," in 2024 International Conference on Electrical Engineering and Computer Science (ICECOS), 2024: IEEE, pp. 1-6.
• [171] C. Li'na, B. Hui, R. Mengmeng, M. Fanlin, Z. Tian, and L. Jing, "Research on fault diagnosis of photovoltaic panels based on deep learning image recognition technology," in International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2024, vol. 13105: SPIE, pp. 91-97.