NDLI: Detection methods for micro-cracked defects of photovoltaic modules based on machine vision

Content Provider	IEEE Xplore Digital Library
Author	Peng Xu Wenju Zhou Minrui Fei
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Electron. Eng., Univ. of Essex, Colchester, UK (Wenju Zhou) \|\| Shanghai Key Lab. of Power Station Autom. Technol., Shanghai Univ., Shanghai, China (Peng Xu; Minrui Fei)
Abstract	The efficiency and the service life of the photovoltaic modules are affected by the surface defects. Therefore, it is critical to detect the photovoltaic modules whether it is qualified or not before assembling into solar panels. This paper applies a method to detect micro-cracked defects in photovoltaic modules using electroluminescence (EL) technology and image processing. After applying forward bias voltage to photovoltaic modules, a large amount of non-equilibrium carriers is injected into photovoltaic modules from the diffusion region recombination to constantly composite luminescence and emit photons. Then an image is formed by a CCD camera which is used to capture these photons. As the brightness of the captured image is proportional to minority carrier diffusion length and current density, if the minority carrier diffusion length is relatively low, there may be defective, which results in a relatively dark image. Micro-cracked defects can be effectively found by analyzing the EL image. Varies of methods, including image segmentation, Gauss filtering, Hough line detection, are used to process image to judge whether the solar cell module is cracked. According to detecting results, the combination of these methods can effectively detect micro-cracked defects in photovoltaic modules.
Starting Page	609
Ending Page	613
File Size	825244
Page Count	5
File Format	PDF
ISBN	9781479947201
e-ISBN	9781479947195
DOI	10.1109/CCIS.2014.7175807
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industries Computers Image segmentation Electroluminescence Detection Automation Photovoltaic Modules Otsu Method Silicon Loss measurement Machine Vision Photonics Hough Line Detection
Content Type	Text
Resource Type	Article

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