Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation

Gomathy Balasubramani, Venkatesan Thangavelu, Muniraj Chinnusamy, Umashankar Subramaniam, Sanjeevikumar Padmanaban and Lucian Mihet-Popa
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Gomathy Balasubramani: Department of Electrical and Electronics Engineering, Paavai College of Engineering, Namakkal 637018, India
Venkatesan Thangavelu: Department of Electrical and Electronics Engineering, K.S. Rangasamy College of Technology, Tiruchenogode 637215, India
Muniraj Chinnusamy: Department of Electrical and Electronics Engineering, Knowledge Institute of Technology, Salem 637504, India
Umashankar Subramaniam: Renewable Energy Lab (REL), Prince Sultan University, Riyadh 12435, Saudi Arabia
Sanjeevikumar Padmanaban: Department of Energy Technology, Aalborg 10 University, 6700 Esbjerg, Denmark
Lucian Mihet-Popa: Faculty of Engineering, Østfold University College, Kobberslagerstredet 5, 1671 Kråkeroy-Fredrikstad, Norway

Energies, 2020, vol. 13, issue 6, 1-14

Abstract: Infrared Thermography has been used as a tool for predictive and preventive maintenance of Photovoltaic panels. International Electrotechnical Commission provides some guidelines for using thermography to detect defects in Photovoltaic panels. However, the proposed guidelines focus only on the location of the hot spot than diagnosing the types of faults. The long-term reliability and efficiency of panels can be affected by progressive defects such as discolouring and delamination. This paper proposed the new Thermal Pixel Counting algorithm to detect the above faults based on three thermal profile index values. The real-time experimental testing was carried out using FLIR T420bx ® thermal imager and results have been provided to validate the proposed method. In this work, the fuzzy rule-based classification system is proposed to automate the classification process. Fuzzy reasoning method based on a single winner rule fuzzy classifier is designed with modified rule weights by particular grade. The performance of the proposed classifier is compared with the conventional fuzzy classifier and neural network model.

Keywords: infrared thermography; photovoltaic panels, discoloring; delamination; defect diagnosis; fuzzy classifier (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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