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Computation
Volume 13
Issue 5
10.3390/computation13050125
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines

by
Yuxiang Liu
1,†,
Xinzhong Xia
2,†,
Jingyang Zhang
1,
Kun Wang
2,
Bo Yu
3,*,
Mengmeng Wu
2,
Jinchao Shi
3,
Chao Ma
2,
Ying Liu
2,
Boyang Hu
2,
Xinying Wang
2,
Bo Wang
1,
Ruzhi Wang
1 and
Bing Wang
1,*
1
College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
2
Yingli Energy (China) Co., Ltd., Baoding 071051, China
3
National Key Laboratory of Photovoltaic Materials and Cells, Yingli Energy Development Co., Ltd., Baoding 071000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Computation 2025, 13(5), 125; https://doi.org/10.3390/computation13050125
Submission received: 8 April 2025 / Revised: 15 May 2025 / Accepted: 16 May 2025 / Published: 20 May 2025
(This article belongs to the Topic Advances in Computational Materials Sciences)
Versions Notes

Abstract

This research introduces a novel hybrid machine learning framework for automated quality prediction and classification of silicon solar modules in production lines. Unlike conventional approaches that rely solely on encapsulation loss rate (ELR) for performance evaluation—a method limited to assessing encapsulation-related power loss—our framework integrates unsupervised clustering and supervised classification to achieve a comprehensive analysis. By leveraging six critical performance parameters (open circuit voltage (VOC), short circuit current (ISC), maximum output power (Pmax), voltage at maximum power point (VPM), current at maximum power point (IPM), and fill factor (FF)), we first employ k-means clustering to dynamically categorize modules into three performance classes: excellent performance (ELR: 0–0.77%), good performance (0.77–8.39%), and poor performance (>8.39%). This multidimensional clustering approach overcomes the narrow focus of traditional ELR-based methods by incorporating photoelectric conversion efficiency and electrical characteristics. Subsequently, five machine learning classifiers—decision trees (DT), random forest (RF), k-nearest neighbors (KNN), naive Bayes classifier (NBC), and support vector machines (SVMs)—are trained to classify modules, achieving 98.90% accuracy with RF demonstrating superior robustness. Pearson correlation analysis further identifies VOC, Pmax, and VPM as the most influential quality determinants, exhibiting strong negative correlations with ELR (−0.953, −0.993, −0.959). The proposed framework not only automates module quality assessment but also enhances production line efficiency by enabling real-time anomaly detection and yield optimization. This work represents a significant advancement in solar module evaluation, bridging the gap between data-driven automation and holistic performance analysis in photovoltaic manufacturing.
Keywords: machine learning; solar module; encapsulation loss rate; energy machine learning; solar module; encapsulation loss rate; energy

Share and Cite

MDPI and ACS Style

Liu, Y.; Xia, X.; Zhang, J.; Wang, K.; Yu, B.; Wu, M.; Shi, J.; Ma, C.; Liu, Y.; Hu, B.; et al. Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines. Computation 2025, 13, 125. https://doi.org/10.3390/computation13050125

AMA Style

Liu Y, Xia X, Zhang J, Wang K, Yu B, Wu M, Shi J, Ma C, Liu Y, Hu B, et al. Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines. Computation. 2025; 13(5):125. https://doi.org/10.3390/computation13050125

Chicago/Turabian Style

Liu, Yuxiang, Xinzhong Xia, Jingyang Zhang, Kun Wang, Bo Yu, Mengmeng Wu, Jinchao Shi, Chao Ma, Ying Liu, Boyang Hu, and et al. 2025. "Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines" Computation 13, no. 5: 125. https://doi.org/10.3390/computation13050125

APA Style

Liu, Y., Xia, X., Zhang, J., Wang, K., Yu, B., Wu, M., Shi, J., Ma, C., Liu, Y., Hu, B., Wang, X., Wang, B., Wang, R., & Wang, B. (2025). Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines. Computation, 13(5), 125. https://doi.org/10.3390/computation13050125

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