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Real-time inspection and determination methods of faults on photovoltaic power systems by thermal imaging in Turkey

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  • Cubukcu, M.
  • Akanalci, A.

Abstract

In recent five years, the capacity of photovoltaic power systems (PVPS) based on solar energy had a significant improvement in Turkey. Approximately 1.8 GWp PVPS was installed in 2017 and reached to 5.062 GWp cumulative capacity at the end of the year 2018. In this development phase, it is critical to ensure a long-term high quality of PV power generation. To achieve a sustainable development, the quality inspections has to be in place. It assures that expected commercial gains are caught and PV plants achieve a long service life. On-site testing of PVPS is very helpful to evaluate the real performance of the plant and diagnose the failures. When compared to other inspection methods, the most time-efficient one is infrared thermal imaging (IRT) of the PV modules and also the electrical components. In this study, the results of IRT inspection for 19 different PVPSs in Turkey (115.36 MWp in total) is presented. The diagnosed defects were classified according to the failure type and the number of them were normalized per MWp to evaluate the most frequently observed issues during the field tests. The experimentation, both in terms of accuracy and processing time, confirms the effectiveness and the efficiency of the thermal imaging approach. The results also showed that the sampling rate of testing is very critical to get a right comparison and failure types are mainly caused due to active bypass diodes and hotspots on PV modules, non-operative modules and strings, abnormally heated cables/fuses/breakers and interconnection points.

Suggested Citation

  • Cubukcu, M. & Akanalci, A., 2020. "Real-time inspection and determination methods of faults on photovoltaic power systems by thermal imaging in Turkey," Renewable Energy, Elsevier, vol. 147(P1), pages 1231-1238.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:1231-1238
    DOI: 10.1016/j.renene.2019.09.075
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    References listed on IDEAS

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    1. Md Saif Hassan Onim & Zubayar Mahatab Md Sakif & Adil Ahnaf & Ahsan Kabir & Abul Kalam Azad & Amanullah Maung Than Oo & Rafina Afreen & Sumaita Tanjim Hridy & Mahtab Hossain & Taskeed Jabid & Md Sawka, 2022. "SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels," Energies, MDPI, vol. 16(1), pages 1-19, December.
    2. Thenmozhi Rajagopal & Amutha Balakrishnan & Sreeram Valsalakumar & Thundil Karuppa Raj Rajagopal & Senthilarasu Sundaram, 2021. "Application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in Drone Thermal Imaging for Solar Farm Monitoring," Energies, MDPI, vol. 14(24), pages 1-16, December.
    3. Kyoik Choi & Jangwon Suh, 2023. "Fault Detection and Power Loss Assessment for Rooftop Photovoltaics Installed in a University Campus, by Use of UAV-Based Infrared Thermography," Energies, MDPI, vol. 16(11), pages 1-16, June.
    4. Hocine, Labar & Samira, Kelaiaia Mounia & Tarek, Mesbah & Salah, Necaibia & Samia, Kelaiaia, 2021. "Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators," Renewable Energy, Elsevier, vol. 164(C), pages 603-617.
    5. Alberti, A.R. & Neto, W.A. Ferreira & Cavalcante, C.A.V. & Santos, A.C.J., 2022. "Modelling a flexible two-phase inspection-maintenance policy for safety-critical systems considering revised and non-revised inspections," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

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