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.
Highlights
A Photovoltaic (PV) panel defects reduce the panel power and long-term reliability that is not recovered during regular operation
The fuzzy classifying the progressive defects such as Ethylene Vinyl Acetate (EVA)-discoloring and delamination through thermal classifier with CF gives better classification accuracy compared with other methods due to its imaging technique is challenging one duepanels to atmospheric classificationtemperature test results. variations and camera noise
In this paper proposed the TPC algorithm to detect the EVA discolouring and delamination defects
Summary
A Photovoltaic (PV) panel defects reduce the panel power and long-term reliability that is not recovered during regular operation. Current-Voltage (I-V) characteristics measurement is used for faults diagnosis on solar PV panels It is a time-consuming process as well as inability to classify the defects such as delamination, EVA discolouring and cell part isolation due to cell cracks. [6] reviewed the IRT image-based fault detection methods used for electrical apparatus maintenances, with thermal image measurement technique and its features extraction, the impact of environmental factors and real-time operating conditions in image measurement. The proposed diagnosis technique is developed based on the method have been proposed in [24] for faults classification of induction motor This algorithm can be implantable in the digital processor, which is used in automated condition monitoring and defect diagnosis system.
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