Solar cell defect standard diagram

How to automatically detect and classify defects in solar cells?

An adaptive approach to automatically detect and classify defects in solar cells is proposed based on absolute electroluminescence (EL) imaging. We integrate the convenient automatic detection algorithm with the effective defect diagnosis solution so that in-depth defect detection and classification becomes feasible.

How to detect a solar cell defect?

An automatic method is proposed for solar cell defect detection and classification. An unsupervised algorithm is designed for adaptive defect detection. A standardized diagnosis scheme is developed for statistical defect classification. Extensive experimental results verify the effectiveness of the proposed method.

How are marked defects classified in GaAs solar cells?

It can be seen that excellent classification results are demonstrated by comparing the extracted ηx’,y’ and simulated η’x’,y’. For GaAs solar cells #1 and #2 in Fig. 7 (a) and (c), the type of marked defects is classified as increasing Rs with the range from 365 to 700 Ω·□ and 300–365 Ω·□, respectively.

Why are local defects common in solar cells?

However, local defects are ubiquitous in solar cells due to the inherently granular structure and specific procedures employed during their manufacturing, which greatly impair the spatial uniformity and overall conversion efficiency of solar cells [ , , , ].

What are solar cell defect characterization methods?

2.3. Proposed solar cell defect detection and classification method Solar cell defect characterization: Generally, the local defects are shown up as dark spots in solar cell EL images, other defect shapes such as micro-crack, large-area failure, break, and finger-interruption are simply regarded as continuous dark spots [ 20, 21, 51, 53 ].

What is adaptive automatic solar cell defect detection & classification method?

The proposed adaptive automatic solar cell defect detection and classification method mainly consists of the following three steps: solar cell EL image preprocessing, adaptive solar cell defect detection, and solar cell defect classification, as shown in Fig. 1.