Novel Features Extraction for Fault Detection Using Thermography Characteristics and IV Measurements of CIGS Thin-Film Module

Abstract

Regarding the fault diagnosis of Copper Indium Gallium Selenide (CIGS) PV modules, previously published articles focused on employing statistical analysis of thermography images. This approach failed in many cases to distinguish among fault types. This article presents a novel methodology to diagnose and predict faults of thin-film CIGS PV modules using infrared thermography analysis combined with measurements of I-V characteristics. The proposed methodology encompasses a comprehensive site work to capture images that cover many fault types of the PV module under study. The novelty of the technique depends on utilizing processing and analysis of the captured images using new proposed mathematical parameters to extract different faults’ features. Using I-V measurements combined with thermography analysis, the differences between different types of faults are detected. Then, a general classification matrix of CIGS fault detection and diagnosis, using features based on mathematical parameters and IV measurements has been established. Results show that the analysis of the temperature distribution is proved to be insufficient to identify specific modes of different faults. In addition, the proposed procedure for fault detection and classification, which depends on the pattern of faults, can be used for any type of PV module. This results in more reliance on the proposed technique to increase the confidence level of fault detection.

Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme

“…IRT imaging and I-V measurements were conducted three times for each module to reduce measurement uncertainty. In [ 3 , 38 ], the present authors have published a detailed set of IR pictures and I-V curves. Figure 2 shows an example of IRT image and I-V curve for a PV module with Type-B fault.…”

“…The process of features extraction was carried out using the MATLAB platform. Detailed description of these features is reported in [ 3 , 38 ]. The ratio of operating voltage, current, power, fill factor, and efficiency are all features of the I-V characteristic curve.…”

“…The use of mathematical parameters for IR image features extraction was studied in [ 38 ]. These features are calculated using the pixel values of an IR image, as detailed in Section 4.1 .…”

“…These features are calculated using the pixel values of an IR image, as detailed in Section 4.1 . The physical significance of each of these features is explained in [ 38 ]. The GFF, which analyses the temperature variation of pixels depending on the type of fault, is found to be one of the most useful parameters of the diagnosis technique.…”

Bilinear and Bicubic Interpolations for Image Presentation of Mechanical Stress and Temperature Distribution

“…Inspired by the finite element method, Gopalakrishnan and Korttom [11] developed an algorithm for contour plot and interpolation of regularly and irregularly distributed field data. Eltuhamy et al [12] innovatively utilized infrared thermography image analysis to detect and predict the fault features of CIGS PV modules. The novelty of their technique manifests in the utilization and analysis of the captured thermal images.…”