Automatic solar cell diagnosis and treatment

Author:

Rodriguez Alvaro,Gonzalez CarlosORCID,Fernandez Andres,Rodriguez Francisco,Delgado Tamara,Bellman Martin

Abstract

AbstractSolar cells represent one of the most important sources of clean energy in modern societies. Solar cell manufacturing is a delicate process that often introduces defects that reduce cell efficiency or compromise durability. Current inspection systems detect and discard faulty cells, wasting a significant percentage of resources. We introduce Cell Doctor, a new inspection system that uses state of the art techniques to locate and classify defects in solar cells and performs a diagnostic and treatment process to isolate or eliminate the defects. Cell Doctor uses a fully automatic process that can be included in a manufacturing line. Incoming solar cells are first moved with a robotic arm to an Electroluminescence diagnostic station, where they are imaged and analysed with a set of Gabor filters, a Principal Component Analysis technique, a Random Forest classifier and different image processing techniques to detect possible defects in the surface of the cell. After the diagnosis, a laser station performs an isolation or cutting process depending on the detected defects. In a final stage, the solar cells are characterised in terms of their I–V Curve and I–V Parameters, in a Solar Simulator station. We validated and tested Cell Doctor with a labelled dataset of images of monocrystalline silicon cells, obtaining an accuracy and recall above 90% for Cracks, Area Defects and Finger interruptions; and precision values of 77% for Finger Interruptions and above 90% for Cracks and Area Defects. Which allows Cell Doctor to diagnose and repair solar cells in an industrial environment in a fully automatic way.

Funder

H2020 LEIT Advanced Manufacturing and Processing

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Cost-Effective Electroluminescence Imaging for Solar PV Inspection Using a Modified Consumer Camera;2023 International Conference on Electrical, Computer and Energy Technologies (ICECET);2023-11-16

2. Comparative Analysis of Defective Solar PV Module Inspection Using Thermal Infrared and Electroluminescence Imaging Techniques;2023 4th International Conference on High Voltage Engineering and Power Systems (ICHVEPS);2023-08-06

3. A Deep Learning-Based Framework for Automatic Detection of Defective Solar Photovoltaic Cells in Electroluminescence Images Using Transfer Learning;2023 4th International Conference on High Voltage Engineering and Power Systems (ICHVEPS);2023-08-06

4. Detection of microcracks in silicon solar cells using Otsu-Canny edge detection algorithm;Renewable Energy Focus;2022-12

5. A Morphology and Coordinate Fusion-Based Positioning Method for Solar Cell Classification;IEEE Sensors Journal;2022-10-15

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