About this Journal Submit a Manuscript Table of Contents
International Journal of Photoenergy
Volume 2013 (2013), Article ID 938162, 8 pages
http://dx.doi.org/10.1155/2013/938162
Research Article

Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan

Received 14 August 2012; Accepted 16 November 2012

Academic Editor: Mahmoud M. El-Nahass

Copyright © 2013 Neng-Sheng Pai 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. S. Schell, “Design and evaluation of esolar's heliostat fields,” Solar Energy, vol. 85, no. 4, pp. 614–619, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. K.-K. Chong and M. H. Tan, “Comparison study of two different sun-tracking methods in optical efficiency of heliostat field,” International Journal of Photoenergy, vol. 2012, Article ID 908364, 10 pages, 2012. View at Publisher · View at Google Scholar
  3. D. Fontani, P. Sansoni, F. Francini, D. Jafrancesco, L. Mercatelli, and E. Sani, “Pointing sensors and sun tracking techniques,” International Journal of Photoenergy, vol. 2011, Article ID 806518, 9 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Wei, Z. Lu, W. Yu, and Z. Wang, “A new code for the design and analysis of the heliostat field layout for power tower system,” Solar Energy, vol. 84, no. 4, pp. 685–690, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Wei, Z. Lu, Z. Wang, W. Yu, H. Zhang, and Z. Yao, “A new method for the design of the heliostat field layout for solar tower power plant,” Renewable Energy, vol. 35, no. 9, pp. 1970–1975, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. K. K. Chong and M. H. Tan, “Range of motion study for two different sun-tracking methods in the application of heliostat field,” Solar Energy, vol. 85, no. 9, pp. 1837–1850, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Sánchez and M. Romero, “Methodology for generation of heliostat field layout in central receiver systems based on yearly normalized energy surfaces,” Solar Energy, vol. 80, no. 7, pp. 861–874, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Cheng-Yu, W. Ding-Sheng, and G. Tie-Zheng, “Application of RBF neural network to fault diagnosis in heliostats filed,” Information Technology 2011-01.
  9. Y. Yang and W. Tang, “Study of remote bearing fault diagnosis based on BP neural network combination,” in Proceedings of the 7th International Conference on Natural Computation, vol. 2, pp. 618–621, 2011.
  10. Z. Yang, W. I. Hoi, and J. Zhong, “Gearbox fault diagnosis based on artificial neural network and genetic algorithms,” in Proceedings of the International Conference onSystem Science and Engineering, pp. 37–42, 2011.
  11. C. P. Hung, M. H. Wang, C. H. Cheng, and W. L. Lin, “Fault diagnosis of steam turbine-generator using CMAC neural network approach,” in Proceedings of the International Joint Conference on Neural Networks, pp. 2988–2993, July 2003. View at Scopus
  12. H. Shiraishi, S. L. Ipri, and D. I. D. Cho, “CMAC neural network controller for fuel-injection systems,” IEEE Transactions on Control Systems Technology, vol. 3, no. 1, pp. 32–38, 1995. View at Publisher · View at Google Scholar · View at Scopus
  13. C. P. Hung and M. H. Wang, “Diagnosis of incipient faults in power transformers using CMAC neural network approach,” Electric Power Systems Research, vol. 71, no. 3, pp. 235–244, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. W. S. Lin, C. P. Hung, and M. H. Wang, “CMAC_based fault diagnosis of power transformers,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '02), vol. 1, pp. 986–991, May 2002. View at Scopus
  15. J. S. Albus, “A new approach to manipulator control: the cerebellar model articulation controller,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 97, no. 3, pp. 220–227, 1975. View at Scopus
  16. D. A. Handelman, S. H. Lane, and J. J. Gelfand, “Integrating neural networks and knowledge-based systems for intelligent robotic control,” IEEE Control Systems Magazine, vol. 10, no. 3, pp. 77–87, 1990. View at Scopus
  17. Y. F. Wong and A. Sideris, “Learning convergence in the cerebellar model articulation controller,” IEEE Transactions on Neural Networks, vol. 3, no. 1, pp. 115–121, 1992. View at Publisher · View at Google Scholar · View at Scopus