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International Journal of Photoenergy
Volume 2017, Article ID 4903613, 14 pages
https://doi.org/10.1155/2017/4903613
Research Article

A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

1Institute of Micro/Nano Devices and Solar Cells, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
2College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Correspondence should be addressed to Shuying Cheng; nc.ude.uzf@gnehcys

Received 28 November 2016; Accepted 6 February 2017; Published 28 March 2017

Academic Editor: Cheuk-Lam Ho

Copyright © 2017 Peijie Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. V. Sharma and S. S. Chandel, “Performance and degradation analysis for long term reliability of solar photovoltaic systems: a review,” Renewable and Sustainable Energy Reviews, vol. 27, pp. 753–767, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. Article 690—Solar Photovoltaic Systems, NFPA70, National Electrical Code, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Zhao, J. F. de Palma, J. Mosesian, R. Lyons, and B. Lehman, “Line–line fault analysis and protection challenges in solar photovoltaic arrays,” IEEE Transactions on Industrial Electronics, vol. 60, no. 9, pp. 3784–3795, 2013. View at Google Scholar
  4. J. Flicker and J. Johnson, “Analysis of fuses for blind spot ground fault detection in photovoltaic power systems,” Sandia National Laboratories Report, Tech. Rep., NM, USA, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Hu, W. Cao, J. Wu, B. Ji, and D. Holliday, “Thermography-based virtual MPPT scheme for improving PV energy efficiency under partial shading conditions,” IEEE Transactions on Power Electronics, vol. 29, no. 11, pp. 5667–5672, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. Z. Zou, Y. Hu, B. Gao, W. L. Woo, and X. Zhao, “Study of the gradual change phenomenon in the infrared image when monitoring photovoltaic array,” Journal of Applied Physics, vol. 115, no. 4, pp. 1–11, 2014. View at Google Scholar
  7. C. Buerhop, D. Schlegel, M. Niess, C. Vodermayer, R. Weißmann, and C. J. Brabec, “Reliability of IR-imaging of PV-plants under operating conditions,” Solar Energy Materials and Solar Cells, vol. 107, pp. 154–164, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Takashima, J. Yamaguchi, K. Otani, T. Oozeki, K. Kato, and M. Ishida, “Experimental studies of fault location in PV module strings,” Solar Energy Materials and Solar Cells, vol. 93, no. 6, pp. 1079–1082, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Takashima, J. Yamaguchi, and M. Ishida, “Fault detection by signal response in PV module strings,” in 33rd IEEE Photovoltaic Specialists Conference, 2008 (PVSC'08), IEEE, pp. 1–5, California, USA, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Silvestre, A. Chouder, and E. Karatepe, “Automatic fault detection in grid connected PV systems,” Solar Energy, vol. 94, pp. 119–127, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Chouder and S. Silvestre, “Automatic supervision and fault detection of PV systems based on power losses analysis,” Energy Conversion and Management, vol. 51, no. 10, pp. 1929–1937, 2010. View at Google Scholar
  12. W. Chine, A. Mellit, A. M. Pavan, and S. A. Kalogirou, “Fault detection method for grid-connected photovoltaic plants,” Renewable Energy, vol. 66, pp. 99–110, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Silvestre, M. A. da Silva, A. Chouder, D. Guasch, and E. Karatepe, “New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators,” Energy Conversion and Management, vol. 86, pp. 241–249, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Silvestre, S. Kichou, A. Chouder, G. Nofuentes, and E. Karatepe, “Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions,” Energy, vol. 86, pp. 42–50, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Silvestre, L. Mora-López, S. Kichou, F. Sánchez-Pacheco, and M. Dominguez-Pumar, “Remote supervision and fault detection on OPC monitored PV systems,” Solar Energy, vol. 137, pp. 424–433, 2016. View at Publisher · View at Google Scholar
  16. I. Yahyaoui and M. E. V. Segatto, “A practical technique for on-line monitoring of a photovoltaic plant connected to a single-phase grid,” Energy Conversion and Management, vol. 132, pp. 198–206, 2017. View at Publisher · View at Google Scholar
  17. Y. Zhao, L. Yang, B. Lehman, J. F. de Palma, J. Mosesian, and R. Lyons, “Decision tree-based fault detection and classification in solar photovoltaic arrays,” in Twenty-Seventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), 2012, IEEE, pp. 93–99, Florida, USA, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Riley and J. Johnson, “Photovoltaic prognostics and health management using learning algorithms,” in 38th IEEE Photovoltaic Specialists Conference (PVSC), 2012, IEEE, pp. 001535–001539, Texas, USA, 2012.
  19. S. Syafaruddin, E. Karatepe, and T. Hiyama, “Controlling of artificial neural network for fault diagnosis of photovoltaic array,” in 16th International Conference on Intelligent System Application to Power Systems (ISAP), 2011, IEEE, pp. 1–6, Hersonissos, Greece, 2011.
  20. Y. Wang, Z. Li, C. Wu, D. Q. Zhou, and L. Fu, “A survey of online fault diagnosis for PV module based on BP neural network,” Power System Technology, vol. 37, no. 8, pp. 2094–2100, 2013. View at Google Scholar
  21. A. M. Pavan, A. Mellit, D. De Pieri, and S. A. Kalogirou, “A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants,” Applied Energy, vol. 108, pp. 392–401, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Chine, A. Mellit, V. Lughi, A. Malek, G. Sulligoi, and A. M. Pavan, “A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks,” Renewable Energy, vol. 90, pp. 501–512, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Zhao, R. Ball, J. Mosesian, J. F. de Palma, and B. Lehman, “Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays,” IEEE Transactions on Power Electronics, vol. 30, no. 5, pp. 2848–2858, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Fang, X. Yin, Y. Tan et al., “The contributions of cloud technologies to smart grid,” Renewable and Sustainable Energy Reviews, vol. 59, pp. 1326–1331, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Hu, M. Zheng, J. Tan, L. Zhu, and W. Miao, “Intelligent photovoltaic monitoring based on solar irradiance big data and wireless sensor networks,” Ad Hoc Networks, vol. 35, pp. 127–136, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. K. H. Tseng, H. J. Wu, G. H. Lin, and P. T. Cheng, “Establishment and case analysis of a photovoltaic cloud management system,” in IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 2016, IEEE, pp. 831–836, Hefei, China, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. Y. Zhao, B. Lehman, J. F. de Palma, J. Mosesian, and R. Lyons, “Fault analysis in solar PV arrays under: low irradiance conditions and reverse connections,” in 37th IEEE Photovoltaic Specialists Conference (PVSC), 2011, IEEE, pp. 002000–002005, Washington, USA, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Rodriguez and A. Laio, “Clustering by fast search and find of density peaks,” Science, vol. 344, no. 6191, pp. 1492–1496, 2014. View at Google Scholar
  29. Z. A. Bakar, R. Mohemad, A. Ahmad, and M. M. Deris, “A comparative study for outlier detection techniques in data mining,” in 2006 IEEE Conference on Cybernetics and Intelligent Systems, IEEE, pp. 1–6, Bangkok, Thailand, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. S. M. MacAlpine, R. W. Erickson, and M. J. Brandemuehl, “Characterization of power optimizer potential to increase energy capture in photovoltaic systems operating under nonuniform conditions,” IEEE Transactions on Power Electronics, vol. 28, no. 6, pp. 2936–2945, 2013. View at Google Scholar
  31. SHELL, Shell SM55 Photovoltaic Solar Module, http://www.solarquest.com/microsolar/suppliers/siemens/sm55.pdf
  32. E. Skoplaki and J. A. Palyvos, “Operating temperature of photovoltaic modules: a survey of pertinent correlations,” Renewable Energy, vol. 34, no. 1, pp. 23–29, 2009. View at Publisher · View at Google Scholar · View at Scopus