Open Access
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
Article Number 03065
Number of page(s) 6
Section Computing
Published online 05 May 2022
  1. HIGUCHI and Tadatoshi BABASAKI, Failure detection of solar panels using thermographic images captured by drone, 7th International Conference on Renewable energy Research and application. [Google Scholar]
  2. A. Triki-Lahiani, A. Bennani-Ben Abdelghani, and I. Slama-Belkhodja, “Fault detection and monitoring systems for photovoltaic installations: a review,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 2680–2692, 2018. [CrossRef] [Google Scholar]
  3. An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles, Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China. [Google Scholar]
  4. Aicha Djalab, Mohamed Mounir Rezaouil, Lakhdar Mazouzl, Ali Tetal, Nassim Sabri2Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks, Periodica Polytechnica Electrical Engineering and Computer Science, 64(3), pp. 291–302, 2020 [Google Scholar]
  5. M. Sabbaghpur Arani and M. A. Hejazi, “The comprehensive study of electrical faults in PV arrays,” Journal of Electrical and Computer Engineering, vol. 2016, Article ID 8712960, 10 pages, 2016. [CrossRef] [Google Scholar]
  6. Köntges, M., Kurtz, S., Jahn, U., Berger, K., Kato, K., Friesen, T., et al.: Review of failures of photovoltaic modules. In: IEA PVPS Task, p. 13 (2014). [Google Scholar]
  7. Haque, A., Bharath, K.V.S., Khan, M.A., Khan, I., Jaffery, Z.A.: Fault diagnosis of photovoltaic modules. Energy Sci Eng 7(3), 622–644 (2019). [CrossRef] [Google Scholar]
  8. Hao, Q., Shao, S., Lu, L., Liu, X., Zhu, H.: A new PV array fault diagnosis method using fuzzy C-mean clustering and fuzzy membership algorithm. Energies 11(1), 238 (2018). [CrossRef] [Google Scholar]
  9. Dhimish, M., Badran, G.: Photovoltaic hot-spots fault detection algorithm using fuzzy systems. IEEE Trans. Device Mater. Reliab. (2019). [Google Scholar]
  10. DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels, Sachin Mehtal, Amar P. Azad2, Saneem A. Chemmengath2, Vikas Raykar2, and Shivkumar Kalyanaraman, University of Washington, Seattle, WA, USA 2 IBM Research Lab, India. [Google Scholar]
  11. Power loss due to soiling on solar panel: A review Mohammad Reza Maghami a,b,n, Hashim Hizam a,b, Chandima Gomes a, Mohd Amran Radzi a, Mohammad Ismael Rezadad c, Shahrooz Hajighorbani. [Google Scholar]
  12. Masoud Alajmi, Sultan Aljahdali, Sultan Alsaheel, Mohammed Fattah, and Mohammed Alshehri Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays, College of Computers and Information Technology, Taif University Al-Hawiya, Taif 21974, Saudi Arabia. [Google Scholar]
  13. J. A. Tsanakas, D. Chrysostomou, P. N. Botsaris, A. Gasteratos, “Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements,” International Journal of Sustainable Energy (T&F), vol.34, no. 6, 2015. [Google Scholar]
  14. Amaral, Tito G., Vitor Fernão Pires, and Armando J. Pires. “Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA.” Energies 14, no. 21 (2021): 7278. [CrossRef] [Google Scholar]
  15. A. Chouder, S. Silvestre, “Automatic supervision and fault detection of PV systems based on power losses analysis,” Energy Conversion and Management, vol. 51, pp. 1929–1937, 2010. [CrossRef] [Google Scholar]
  16. Jaffery, Z. A., Dubey, A. K., Irshad, & Haque, A. (2017). Scheme for predictive fault diagnosis in photovoltaic modules using thermal imaging. Infrared Physics & Technology, 83, 182–187. DOI:10.1016/j.infrared.2017.04.015. [CrossRef] [Google Scholar]
  17. T. Takashima, J. Yamaguchi, K. Otani, T. Oozeki, K. Kato, M. Ishida, “Experimental studies of fault location in PV module strings,” Solar Energy Materials and Solar Cells, 93 (2009) 1079–1082 [CrossRef] [Google Scholar]
  18. D. Riley, J. Johnson, “Photovoltaic prognostics and heat management using learning algorithms,” IEEE 38th Conference (PVSC), 2012, pp. 001535–001539. [Google Scholar]
  19. P. Ducange, M. Fazzolari, B. Lazzerini, F. Marcelloni, “An intelligent system for detecting faults in photovoltaic fields,” 11th International Conference on Intelligent Systems Design and Applications (ISDA), 2011, pp. 1341–1346. [Google Scholar]
  20. A. Coleman, J. Zalewski, “Intelligent fault detection and diagnostics in solar plants,” IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) 2011, pp. 948–953. [Google Scholar]
  21. Pierdicca, R.; Malinverni, E.; Piccinini, F.; Paolanti, M.; Felicetti, A.; Zingaretti, P. Deep Convolutional neural network for automatic detection of damaged photovoltaic cells. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, Riva del Garda, Italy, 4-7 June 2018; Volume 42. [Google Scholar]
  22. Christopher Dunderdale; Warren Brettenny; Chantelle Clohessy E. Ernest van Dyk, “Photovoltaic defect classification through thermal infrared imaging using a machine learning approach” Nelson Mandela University; South Africa Statistical Association, DOI: 10.1002/pip.3191, Accepted: 20 August 2019. [Google Scholar]
  23. Baba M., Shimakage T. and Takeuchi N., “Examination of fault detection technique in PV systems,” 35th International Telecommunications Energy Conference, 2013. [Google Scholar]
  24. Chen Z.., Wu L.., Cheng S.., Lin P., Wu T., Lin, W. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics. Appl Energy. 2017;204:912–931. [CrossRef] [Google Scholar]
  25. Deitsch S.., Christlein V.., Berger S.., et al. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy. 2019;185:455–468. [CrossRef] [Google Scholar]
  26. Jaffery Z.A.., Dubey A.K.., Haque I.A. Scheme for predictive fault diagnosis in photovoltaic modules using thermal imaging. Infrared Phys Technol. 2017;83:182–187. [CrossRef] [Google Scholar]
  27. Pierdicca R.., Malinverni E.S.., Piccinini F.., Paolanti M.., Felicetti A.., Zingaretti, P. Deep convolutional neural network for automatic detection of damaged photovoltaic cells. Int Arch Photogramm Remote Sens Spat Inf Sci. 2018;42:893–900. [CrossRef] [Google Scholar]

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