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| Research on Photovoltaic Cell Defect Detection and Performance
Evaluation Based on Photoluminescence Imaging |
| PAN Wen-wen1, 2, QIAN Yun-sheng1*, LIU Ya-cheng1, SUN Xiao-fei2* |
1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2. School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China
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Abstract EL imaging and PL imaging are commonly used detection technologies in the photovoltaic industry. EL imaging has high spatial resolution, but requires an external power supply, which increases the difficulty of detection; PL imaging does not require electrical contact. After illuminating the photovoltaic cell with a PL light source, a photoluminescence signal is generated on the photovoltaic cell. By using a high-sensitivity camera to collect the signal, high-resolution image information can be obtained non-contact for defect detection. To verify whether the PL imaging method can be applied to the defect detection of photovoltaic cells and the characterization of photovoltaic cells, this paper establishes the corresponding experimental methods and test processes based on a set of self-developed PL imaging experimental platform, obtains PL images of photovoltaic cells under different excitation conditions by adjusting the input current of LED excitation light source, and uses the open circuit voltage obtained from IV measurement results as a reference, calculates the voltage difference with PL images according to the generalized Planck equation, and then verifies the quantitative analysis capability of PL imaging. The research shows that PL imaging can clearly show scratches and defects caused by materials in photovoltaic cells; The voltage difference calculated by PL imaging has a good linear relationship with the voltage difference measured by IV; PL imaging can completely image the entire photovoltaic cell without electrical contact, and its spatial resolution can reach the same level as EL imaging. Multiple defect types can be detected using coexcitation.
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Received: 2025-03-13
Accepted: 2025-09-22
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Corresponding Authors:
QIAN Yun-sheng, SUN Xiao-fei
E-mail: yshqian@njust.edu.cn；sunxiaofei18@mails.ucas.edu.cn
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