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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 564517, 10 pages
http://dx.doi.org/10.1155/2014/564517
Research Article

Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

1Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 10607, Taiwan
2Department of Electrical Engineering, Saint John’s University, No. 499, Section 4, TamKing Road, Tamsui, Taipei 25135, Taiwan

Received 28 February 2014; Accepted 4 April 2014; Published 3 July 2014

Academic Editor: Her-Terng Yau

Copyright © 2014 Hong-Chan Chang 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. R. D. Tapakis and A. G. Charalambides, “Monitoring cloud motion in cyprus for solar irradiance prediction,” Conference Papers in Energy, vol. 2013, Article ID 320618, 6 pages, 2013. View at Publisher · View at Google Scholar
  2. H. Fang, B. Chen, H. Ma, and L. Zhang, “Intelligent monitoring and predicting output power losses of solar arrays based on particle filtering,” Mathematical Problems in Engineering, vol. 2013, Article ID 819379, 7 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Watson, G. Reynolds, D. Wragg, G. Williams, and D. Worsley, “Corrosion monitoring of flexible metallic substrates for dye-sensitized solar cells,” International Journal of Photoenergy, vol. 2013, Article ID 791438, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Ranhotigamage and S. C. Mukhopadhyay, “Field trials and performance monitoring of distributed solar panels using a low-cost wireless sensors network for domestic applications,” IEEE Sensors Journal, vol. 11, no. 10, pp. 2583–2590, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Coleman and J. Zalewski, “Intelligent fault detection and diagnostics in solar plants,” in Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS ’11), pp. 948–953, Prague, Czech Republic, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Drews, A. C. de Keizer, H. G. Beyer et al., “Monitoring and remote failure detection of grid-connected PV systems based on satellite observations,” Solar Energy, vol. 81, no. 4, pp. 548–564, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. G. M. Masters, Renewable and Efficient Electric Power Systems, John Wiley & Sons, New York, NY, USA, 2004.
  8. K. Ding, X. Bian, H. Liu, and T. Peng, “A MATLAB-simulink-based PV module model and its application under conditions of nonuniform irradiance,” IEEE Transactions on Energy Conversion, vol. 27, no. 4, pp. 864–872, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. U. B. Parikh, B. Das, and R. P. Maheshwari, “Combined wavelet-SVM technique for fault zone detection in a series compensated transmission line,” IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 1789–1794, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Koley, P. Purkait, and S. Chakravorti, “Wavelet-aided SVM tool for impulse fault identification in transformers,” IEEE Transactions on Power Delivery, vol. 21, no. 3, pp. 1283–1290, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Ni, C. Zhang, and S. X. Yang, “An adaptive approach based on KPCA and SVM for real-time fault diagnosis of HVCBs,” IEEE Transactions on Power Delivery, vol. 26, no. 3, pp. 1960–1971, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Shintemirov, W. Tang, and Q. H. Wu, “Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming,” IEEE Transactions on Systems, Man and Cybernetics C: Applications and Reviews, vol. 39, no. 1, pp. 69–79, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. C.-J. Wang, Build power transformer failure diagnosis system by using GSM data acquisition module and multilayer SVM classifier [M.S. thesis], National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, 2005.