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CNN based automatic detection of photovoltaic cell defects in electroluminescence images

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  1. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  2. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  3. Zhang, Jinxia & Chen, Xinyi & Wei, Haikun & Zhang, Kanjian, 2024. "A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation," Applied Energy, Elsevier, vol. 355(C).
  4. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  5. Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
  6. Waqar Akram, M. & Li, Guiqiang & Jin, Yi & Chen, Xiao, 2022. "Failures of Photovoltaic modules and their Detection: A Review," Applied Energy, Elsevier, vol. 313(C).
  7. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
  8. Wang, Haoxuan & Chen, Huaian & Wang, Ben & Jin, Yi & Li, Guiqiang & Kan, Yan, 2022. "High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network," Applied Energy, Elsevier, vol. 318(C).
  9. Tang, Wuqin & Yang, Qiang & Dai, Zhou & Yan, Wenjun, 2024. "Module defect detection and diagnosis for intelligent maintenance of solar photovoltaic plants: Techniques, systems and perspectives," Energy, Elsevier, vol. 297(C).
  10. Cheng Cheng & Ning Dai & Jie Huang & Yahong Zhuang & Tao Tang & Longlong Liu, 2022. "RETRACTED ARTICLE: Capacitance pin defect detection based on deep learning," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3477-3494, December.
  11. Wang, Yun & Duan, Xiaocong & Zou, Runmin & Zhang, Fan & Li, Yifen & Hu, Qinghua, 2023. "A novel data-driven deep learning approach for wind turbine power curve modeling," Energy, Elsevier, vol. 270(C).
  12. Chou, Jui-Sheng & Truong, Dinh-Nhat & Kuo, Ching-Chiun, 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, Elsevier, vol. 224(C).
  13. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
  14. Hassan, Sharmarke & Dhimish, Mahmoud, 2023. "Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection," Renewable Energy, Elsevier, vol. 219(P1).
  15. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
  16. Michael W. Hopwood & Lekha Patel & Thushara Gunda, 2022. "Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach," Energies, MDPI, vol. 15(14), pages 1-12, July.
  17. Zhenying Xu & Ziqian Wu & Wei Fan, 2021. "Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence," Journal of Risk and Reliability, , vol. 235(5), pages 761-768, October.
  18. Shirzad, Hossein & Barati, Ali Akbar & Ehteshammajd, Shaghayegh & Goli, Imaneh & Siamian, Narges & Moghaddam, Saghi Movahhed & Pour, Mahdad & Tan, Rong & Janečková, Kristina & Sklenička, Petr & Azadi,, 2022. "Agricultural land tenure system in Iran: An overview," Land Use Policy, Elsevier, vol. 123(C).
  19. Dávid Matusz-Kalász & István Bodnár, 2021. "Operation Problems of Solar Panel Caused by the Surface Contamination," Energies, MDPI, vol. 14(17), pages 1-13, September.
  20. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  21. Naveen Venkatesh Sridharan & Jerome Vasanth Joseph & Sugumaran Vaithiyanathan & Mohammadreza Aghaei, 2023. "Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules," Energies, MDPI, vol. 16(15), pages 1-17, August.
  22. Ramadoss Janarthanan & R. Uma Maheshwari & Prashant Kumar Shukla & Piyush Kumar Shukla & Seyedali Mirjalili & Manoj Kumar, 2021. "Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems," Energies, MDPI, vol. 14(20), pages 1-19, October.
  23. Wang, Youyang & Li, Liying & Sun, Yifan & Xu, Jinjia & Jia, Yun & Hong, Jianyu & Hu, Xiaobo & Weng, Guoen & Luo, Xianjia & Chen, Shaoqiang & Zhu, Ziqiang & Chu, Junhao & Akiyama, Hidefumi, 2021. "Adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging," Energy, Elsevier, vol. 229(C).
  24. Dhimish, Mahmoud & Ahmad, Ameer & Tyrrell, Andy M., 2022. "Inequalities in photovoltaics modules reliability: From packaging to PV installation site," Renewable Energy, Elsevier, vol. 192(C), pages 805-814.
  25. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
  26. Zhao, Xiaolong & Song, Chonghui & Zhang, Haifeng & Sun, Xianrui & Zhao, Jing, 2023. "HRNet-based automatic identification of photovoltaic module defects using electroluminescence images," Energy, Elsevier, vol. 267(C).
  27. Pratt, Lawrence & Govender, Devashen & Klein, Richard, 2021. "Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation," Renewable Energy, Elsevier, vol. 178(C), pages 1211-1222.
  28. Shijie Wang & Haiyong Chen & Kun Liu & Ying Zhou & Huichuan Feng, 2023. "Meta-FSDet: a meta-learning based detector for few-shot defects of photovoltaic modules," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3413-3427, December.
  29. Aidong Chen & Xiang Li & Hongyuan Jing & Chen Hong & Minghai Li, 2023. "Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism," Energies, MDPI, vol. 16(4), pages 1-15, February.
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