ORCID Profile
0000-0003-1848-4990
Current Organisation
Aalborg Universitet
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Publisher: IEEE
Date: 06-2018
Publisher: IEEE
Date: 06-2017
Publisher: WIP
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: WIP
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: IEEE
Date: 06-2018
Publisher: MDPI AG
Date: 10-12-2020
DOI: 10.3390/APP10248834
Abstract: A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.
Publisher: WIP
Date: 2018
Publisher: WIP
Date: 2017
Publisher: IEEE
Date: 06-2018
Publisher: SPIE
Date: 06-09-2019
DOI: 10.1117/12.2528440
Publisher: IEEE
Date: 06-2018
Publisher: WIP
Date: 2017
Publisher: IEEE
Date: 06-2019
No related grants have been discovered for Harsh Parikh.