Conceptual introduction of a high-resolution image-based defect inspection method for photovoltaic panels

Abstract

Photovoltaic (PV) systems require accessible and reliable inspection approaches to support quality control and defect diagnosis, especially in settings where advanced imaging systems such as electroluminescence may be unavailable, impractical, or cost-intensive. In this context, the present study introduces a systematic high-resolution image-based inspection workflow for the visual assessment of surface defects in PV panels and positions this workflow as a digital twin-oriented inspection layer rather than a fully synchronized digital twin implementation. Under controlled laboratory lighting conditions, defective PV panel regions were photographed using a high-resolution digital camera, and the acquired images were processed through a systematic enhancement workflow including brightness adjustment, contrast optimization, noise reduction, and edge sharpening. The enhanced images improved the visual interpretability of several visible defect categories, including ribbon deflection, EVA bubbles, uncut cells, cell particles, ribbon-tab defects, visible cell fractures, and non-laminated EVA regions. The findings indicate that high-resolution image enhancement can provide a low-cost and portable visual basis for defect-oriented PV inspection by increasing local contrast and defect readability under controlled conditions. However, the proposed approach functions as a qualitative and modular visual foundation for future digital twin integration, rather than as a complete digital twin system.

Keywords

• Photovoltaic defect inspection
• Image enhancement
• Visual diagnostics
• Panel surface anomalies
• Digital twin-oriented inspection

Supporting Institution

The Scientific and Technological Research Council of Türkiye (TÜBİTAK), Scientist Support Programs Presidency (BİDEB)

Project Number

1919B012314529

Thanks

This study was supported by TÜBİTAK BİDEB under the Undergraduate Student Graduation Project Support Program, project/contract number 1919B012314529.

References

1. [1] Barykin SY, Bochkarev AA, Kalinina OV, Yadykin VK. Concept for a supply chain digital twin. International Journal of Mathematical, Engineering and Management Sciences 2020; 5(6): 1498-1515.
2. [2] Boje C, Guerriero A, Kubicki S, Rezgui Y. Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction 2020; 114: 103179.
3. [3] Al Zami MB, Shaon S, Quy VK, Nguyen DC. Digital Twin in industries: A comprehensive survey. IEEE Access 2025; 13: 47291-47336.
4. [4] Al-Shetwi AQ, Atawi IE, El-Hameed MA, Abuelrub A. Digital Twin technology for renewable energy, smart grids, energy storage and vehicle-to-grid integration: Advancements, applications, key players, challenges and future perspectives in modernising sustainable grids. IET Smart Grid 2025; 8(1): e70026.
5. [5] Hassani H, Huang X, MacFeely S. Impactful Digital Twin in the healthcare revolution. Big Data and Cognitive Computing 2022; 6(3): 83.
6. [6] Wright L, Davidson S. How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences 2020; 7(1): 13.
7. [7] Errandonea I, Beltrán S, Arrizabalaga S. Digital Twin for maintenance: A literature review. Computers in Industry 2020; 123: 103316.
8. [8] Kuruppuarachchi PM, Rea S, Wenning BL, McGibney A. Collaborative digital twin ecosystems. Future Generation Computer Systems 2026; 181: 108429.

9. [9] Ye C, Butler L, Calka B, Iangurazov M, Lu Q, Gregory A, Girolami M, Middleton C. A digital twin of bridges for structural health monitoring. 12th International Workshop on Structural Health Monitoring, Stanford, CA, US, 2019.
10. [10] Xia T, Zuo J, Ding J, Pan Y. Degradation-adaptive digital twin for long-term consistency. Simulation Modelling Practice and Theory 2026; 149: 103276.
11. [11] Singh M, Fuenmayor E, Hinchy EP, Qiao Y, Murray N, Devine D. Digital twin: Origin to future. Applied System Innovation 2021; 4(2): 36.
12. [12] VanDerHorn E, Mahadevan S. Digital Twin: Generalization, characterization and implementation. Decision Support Systems 2021; 145: 113524.
13. [13] Fuller A, Fan Z, Day C, Barlow C. Digital Twin: Enabling technologies, challenges and open research. IEEE Access 2020; 8: 108952-108971.
14. [14] Attaran M, Celik BG. Digital Twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal 2023; 6: 100165.
15. [15] Amaran S, Sahinidis NV, Sharda B, Bury SJ. Simulation optimization: A review of algorithms and applications. Annals of Operations Research 2016; 240(1): 351-380.
16. [16] Sargent RG. Verification and validation of simulation models. Proceedings of the 2010 Winter Simulation Conference, Baltimore, MD, US, 2010; 166-183.
17. [17] Biswas P, Kim TM, Kim WS. Integration of digital twins and physical AI in cyber-physical systems. Intelligent Systems with Applications 2026; 30: 200649.
18. [18] Soudagar MEM, et al. AI-enabled digital twins for thermal energy storage in renewable power systems: Multi-scale modelling, power-system integration, and co-simulation frameworks. Electric Power Systems Research 2026; 257: 112947.
19. [19] Agrawal A, Thiel R, Jain P, Singh V, Fischer M. Digital Twin: Where do humans fit in? Automation in Construction 2023; 148: 104749.
20. [20] Jones D, Snider C, Nassehi A, Yon J, Hicks B. Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology 2020; 29: 36-52.
21. [21] Rasheed A, San O, Kvamsdal T. Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access 2020; 8: 21980-22012.
22. [22] Albayrak A. A multi-layered digital twin framework for smart agriculture and livestock: System architecture and implementation challenges. Information and Software Technology 2026; 195: 108139.
23. [23] Horowitz KAW, Nielson G, Ramdas A, Cunningham DW, Majumdar Z, Feldman D, Fu R, Sigrin B. The effect of photovoltaic module efficiency on installed system costs and markets in residential rooftop installations in the United States. NREL/TP-6A20-77471. National Renewable Energy Laboratory, Golden, CO, US, 2020.
24. [24] Keçecioğlu ÖF, Tekin M, Gani A, Açıkgöz H, Gemci A, Şekkeli M. Bir güneş enerji santralinin elektrik şebekesindeki güç kalitesi parametrelerine etkisinin incelenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 2015; 18(2): 17-24.
25. [25] Gani A, Açıkgöz H, Şekkeli M. Fotovoltaik sistemlerde değişken yük ve güneş ışınımı altında sinirsel-bulanık denetleyici ile maksimum güç noktası takibi. European Journal of Science and Technology 2020; 19: 734-745.
26. [26] Yalçin T, Paradell Solà P, Stefanidou-Voziki P, Domínguez-García JL, Demirdelen T. Exploiting digitalization of solar PV plants using machine learning: Digital Twin concept for operation. Energies 2023; 16(13): 5044.
27. [27] Ahsan L, Baig MJA, Iqbal MT. Low-cost, open-source, Emoncms-based SCADA system for a large grid-connected PV system. Sensors 2022; 22(18): 6733.
28. [28] Subramanian N, Stonier AA. Digital twin applications and case studies in modern power grid management. Energy Reports 2026; 15: 109218.
29. [29] Sehrawat N, Vashisht S, Singh A. Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison. International Journal of Intelligent Networks 2023; 4: 90-102.
30. [30] Bhatti G, Mohan H, Raja Singh R. Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews 2021; 141: 110801.
31. [31] do Amaral JVS, dos Santos CH, Montevechi JAB, de Queiroz AR. Energy Digital Twin applications: A review. Renewable and Sustainable Energy Reviews 2023; 188: 113891.
32. [32] Angelova DD, Fernández DC, Godoy MC, Moreno JAÁ, González JFG. A review on Digital Twins and its application in the modeling of photovoltaic installations. Energies 2024; 17(5): 1227.
33. [33] Aghaei M, Kolahi M, Nedaei A, Venkatesh NS, Esmailifar M, Moradi Sizkouhi A, Aghamohammadi A, Oliveira AKV, Eskandari A, Parvin P, Milimonfared J, Sugumaran V, Rüther R. Autonomous intelligent monitoring of photovoltaic systems: An in-depth multidisciplinary review. Progress in Photovoltaics: Research and Applications 2025; 33: 381-409.
34. [34] Barksdale H, Smith Q, Khan MS. Condition monitoring of electrical machines with Internet of Things. SoutheastCon 2018, St. Petersburg, Florida, US, 2018; 1-4.
35. [35] Poon J, Konstantakopoulos IC, Spanos C, Sanders SR. Real-time model-based fault diagnosis for switching power converters. 2015 IEEE Applied Power Electronics Conference and Exposition (APEC), Charlotte, NC, US, 2015; 358-364.
36. [36] Carpintero LA, et al. Electroluminescence inspections of PV modules and strings by a self-powering configuration in daylight mode. Solar Energy 2025; 301: 113913.
37. [37] Redondo-Plaza A, Zorita-Lamadrid ÁL, Alonso-Gómez V, Hernández-Callejo L. Inspection techniques in photovoltaic power plants: A review of electroluminescence and photoluminescence imaging. Renewable Energies 2024; 2(2): 27533735241282603.
38. [38] Machado PLO, et al. A low-cost Arduino-based I-V curve tracer with automated load switching for PV panel characterization. Applied Sciences 2025; 15(15): 8186.
39. [39] Pei FQ, Tong YF, Yuan MH, Ding K, Chen XH. The digital twin of the quality monitoring and control in the series solar cell production line. Journal of Manufacturing Systems 2021; 59: 127-137.
40. [40] Ferlito S, Ippolito S, Santagata C, Schiattarella P, Di Francia G. A study on an IoT-based SCADA system for photovoltaic utility plants. Electronics 2024; 13(11): 2065.
41. [41] Zhao X. A novel digital-twin approach based on transformer for photovoltaic power prediction. Scientific Reports 2024; 14: 26661.
42. [42] Liu YM, Kuo CC, Chen HC. A hybrid GA-Digital Twin strategy for real-time nighttime reactive power compensation in utility-scale PV plants. Applied Sciences 2025; 15(20): 11282.
43. [43] Farrokhi Derakhshandeh J, et al. A comprehensive review of automatic cleaning systems of solar panels. Sustainable Energy Technologies and Assessments 2021; 47: 101518.
44. [44] Horowitz KAW, Nielson G, Ramdas A, Cunningham DW, Majumdar Z, Feldman D, Fu R, Sigrin B. The effect of photovoltaic module efficiency on installed system costs and markets in residential rooftop installations in the United States. 2020. [Online] Available from: https://www.nrel.gov/docs/fy21osti/77471.pdf (Accessed: 15/04/2026).
45. [45] nternational Energy Agency. Renewables 2025. IEA, Paris, 2025. [Online] Available from: https://www.iea.org/reports/renewables-2025 (Accessed: 15/04/2026).
46. [46] Elnosh A, Calais M, Parlevliet D. A systematic literature review of digital twin research for photovoltaic systems: Trends, challenges, and opportunities. Renewable and Sustainable Energy Reviews 2026; 226: 116326.
47. [47] Louwen A, et al. Digitalisation and Digital Twins in Photovoltaic Systems. IEA PVPS Task 13, Report IEA-PVPS T13-34:2026, 2026.
48. [48] Deitsch S, Christlein V, Berger S, Buerhop-Lutz C, Maier A, Gallwitz F, Riess C. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy 2019; 185: 455-468.