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

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.