Classification of Cell Line Halm Machine Data in Solar Energy Panel Production Factories Using Artificial Intelligence Models

Öz

This study presents a quality estimation method for photovoltaic cells in solar panels using advanced machine learning techniques, including traditional methods and convolutional neural networks (CNNs). Photovoltaic cells, which are primarily composed of crystalline silicon, are of critical importance for the conversion of sunlight into electrical energy. The efficiency of these cells directly affects the performance and lifespan of solar panels. The study’s objective is to assess the electroluminescence values of cells using the HALM device, which is capable of measuring the key parameters that determine cell quality. To enhance the performance of the CNN model, hyperparameter tuning and optimization techniques were employed to improve the accuracy of visual evaluation and classification. The proposed method offers significant advantages, including the optimization of the cell production process, a reduction in costs, and an improvement in operational efficiency through the minimization of discrepancies between human and machine decisions. Furthermore, this approach facilitates real-time monitoring and dynamic management of production processes by integrating machine learning models with production line databases. The findings indicate the potential of artificial intelligence to enhance the detection and classification of cell defects, thereby supporting more efficient and high-quality solar panel production. The study underscores the importance of AI-driven methods in advancing production technologies and improving the sustainability of solar energy systems.

Anahtar Kelimeler

• Artificial Intelligence
• Machine Learning
• Solar Energy
• Pv-Photovoltaics
• Energy Quality.

Classification of Cell Line Halm Machine Data in Solar Energy Panel Production Factories Using Artificial Intelligence Models

Öz

This study presents a quality estimation method for photovoltaic cells in solar panels using advanced machine learning techniques, including traditional methods and convolutional neural networks (CNNs). Photovoltaic cells, primarily made of crystalline silicon, are critical for converting sunlight into electrical energy, and their efficiency directly affects the performance and lifespan of solar panels. The study focuses on evaluating the electroluminescence values of cells using the HALM device, which measures key parameters that determine cell quality. To enhance the CNN model’s performance, hyperparameter tuning and optimization techniques were applied to improve visual evaluation and classification accuracy. The proposed method offers significant advantages, such as optimizing the cell production process, reducing costs, and improving operational efficiency by minimizing human-machine decision discrepancies. Additionally, this approach enables real-time monitoring and dynamic management of production processes by integrating machine learning models with production line databases. The findings highlight the potential of artificial intelligence to enhance the detection and classification of cell defects, thereby supporting more efficient, high-quality solar panel production. The study underscores the importance of AI-driven methods in advancing production technologies and improving the sustainability of solar energy systems.

Anahtar Kelimeler

• Artificial Intelligence
• Machine Learning
• Solar Energy
• Pv-Photovoltaics
• Energy Quality
• Sigma Principles

Kaynakça

1. [1] W.-C. Hong, P.-F. Pai, Y.-Y. Huang, and S.-L. Yang, ‘‘Application of support vector machines in predicting employee turnover based on job performance,’’ in Advances in Natural Computation, 2005, pp. 668–674.
2. [2] B. L. Aylak, O. Oral, and K. Yazıcı, ‘‘Yapay zeka ve makine Öğrenmesi tekniklerinin lojistik sektöründe kullanımı,’’ El-Cezeri Journal of Science and Engineering, vol. 8, no. 1, pp. 74–93, 2021.
3. [3] M. A. Green, Solar Cells: Operating Principles, Technology, and System Applications. University of New South Wales Press, 2018.
4. [4] E. E. Antunez, J. Gonzalez-Hernandez, and A. Dominguez, ‘‘Artificial neural network modeling of a photovoltaic panel considering temperature effects,’’ International Journal of Energy Research, vol. 43, no. 12, pp. 5939–5950, 2019.
5. [5] H. H. Al-Kayiem and Z. S. Al-Khafaji, ‘‘Modeling and optimization of photovoltaic cells using artificial neural networks: A review,’’ Renewable and Sustainable Energy Reviews, vol. 82, pp. 1811–1820, 2018.
6. [6] Y. Ma, Y. Yang, X. Yu, Y. Chen, and F. Blaabjerg, ‘‘Artificial intelligence for energy management in future smart grids: A review,’’ Renewable and Sustainable Energy Reviews, vol. 104, pp. 62–72, 2019.
7. [7] X. e. a. Liu, ‘‘Automated defect detection for photovoltaic modules using convolutional neural networks,’’ IEEE Transactions on Industrial Informatics, 2020.
8. [8] M. Garcia et al., ‘‘Principal component analysis for solar cell defect detection,’’ in Procedia Manufacturing, 2018, pp. 34–49.

9. [9] A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, ‘‘Improving language understanding by generative pre-training,’’ 2018.
10. [10] S. Sharma et al., ‘‘Random forest-based approach for solar cell quality prediction,’’ Renewable Energy, pp. 3–7, 2017.
11. [11] J. Zhang et al., ‘‘Transfer learning for defect detection in solar cells using deep convolutional neural networks,’’ Solar Energy, 2019.
12. [12] D. P. Kingma, ‘‘Adam: A method for stochastic optimization,’’ 2014, arXiv preprint.
13. [13] F. Itano, M. A. de Abreu de Sousa, and E. Del-Moral-Hernandez, ‘‘Extending mlp ann hyperparameters optimization by using genetic algorithm,’’ in Proceedings of the 2018 International Joint Conference, 2018, pp. 66–67.
14. [14] K. Ramspeck, S. Schenk, L. Komp, A. Metz, and M. Meixner, ‘‘Accurate efficiency measurements on very high efficiency silicon solar cells using pulsed light sources,’’ in Proceedings of the 29th European Photovoltaic Solar Energy Conference and Exhibition, 2014, pp. 1253–1256.
15. [15] Z. Lu and J. Wu, ‘‘A review of artificial intelligence applications in marine renewable energy systems,’’ Renewable and Sustainable Energy Reviews, vol. 117, p. 109469, 2020.
16. [16] C. Monokroussos, R. Gottschalg, A. N. Tiwari, G. Friesen, D. Chianese, and S. Mau, ‘‘The effects of solar cell capacitance on calibration accuracy when using a flash simulator,’’ in Proceedings of the 4th WCPSEC, vol. 2, 2006, pp. 2231–2234.
17. [17] C. Y. J. Peng, K. L. Lee, and G. M. Ingersoll, ‘‘An introduction to logistic regression analysis and reporting,’’ The Journal of Educational Research, vol. 96, no. 1, pp. 3–14, 2002.
18. [18] G. Rong, G. Mendez, E. B. Assi, B. Bajic, and N. V. Chawla, ‘‘Understanding sbml’s flat line: An analysis of loss functions for oversampling-based classifiers in imbalanced datasets,’’ in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 5372–5379.