Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Fault Detection System for Wind Power Generator Based on Intelligent Clustering Method

지능형 클러스터링 기법에 기반한 풍력발전 고장 검출 시스템

  • 문대선 (군산대학교 전자정보공학부) ;
  • 김선국 (군산대학교 전자정보공학부) ;
  • 김성호 (군산대학교 제어로봇공학과)
  • Received : 2012.11.24
  • Accepted : 2012.12.26
  • Published : 2013.01.01
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Abstract

Nowadays, the utilization of renewable energy sources like wind energy is considered one of the most effective means of generating massive amounts of electricity. This is evident in the rapid increase of wind farms all over the world which comprise a huge number of wind turbines. However, the drawback of utilizing wind turbines is that it requires maintenance, which could be a costly operation. To keep the wind turbines in pristine condition so as to reduce downtime, the implementation of CMS (Condition Monitoring System) and FDS (Fault Detection System) is mandatory. The efficiency and accuracy of these systems are crucial in deciding when to carry out a maintenance process. In this paper, a fault detection system based on intelligent clustering method is proposed. Using SCADA data, the clustering model was trained and evaluated for its accuracy through rigorous simulations. Results show that the proposed approach is able to accurately detect the deteriorating condition of a wind turbine as it nears a downtime period.

Keywords

References

  1. S. D. Oh, "Current development trend of wind turbine system," Journal of Fluid Machinery, vol. 8, no. 3, pp. 65-72, 2005.
  2. R. W. Hyers and J. G. McGowan, "Condition monitoring and prognosis of utility scale wind turbine," Energy Material, vol. 1, no. 3, pp. 187-203, 2006. https://doi.org/10.1179/174892406X163397
  3. M. Lucente, "Condition monitoring system in wind turbine gearbox," Master's Degree Thesis in KTH Electrical Engineering, 2008.
  4. Z. Hameed, Y. S. Hong, Y. M. Cho, S. H. Ahn, and C. K. Song, "Condition monitoring and fault detection of wind turbines and related algorithms: a review," Renewable Sustainable Energy Reviews, vol. 13, no. 1, pp. 1-39, 2009. https://doi.org/10.1016/j.rser.2007.05.008
  5. A. Zaher and D. D. J. McArthur, "Online wind turbine fault detection through automated SCADA data analysis," Wind Energy, vol. 12, pp. 574-593, 2009. https://doi.org/10.1002/we.319
  6. E. Lapira and D. Brisset, "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, vol. 45, pp. 86-95, 2012. https://doi.org/10.1016/j.renene.2012.02.018
  7. D. S. Moon and S. H. Kim, "Development of clustering technique based wind turbine fault detection system," 2012 ICROS Jeonbuk and Jeju Regional conference (in Korean), vol. 1, pp. 205-207, 2012.
  8. J. C. Bezdek, "Pattern recognition with fuzzy objective function algorithms," Plenum Press, 1981.
  9. D. E. Gustafson and W. C. Kessel, "Fuzzy clustering with a fuzzy covariance matrix," In Proc. of the IEEE CDC, pp. 761-766, San Diego, CA, USA, 1978.
  10. R. Babuska, P. J. van der veen, and U. Kayma, "Improved covariance estimation for Gustafson-Kessel clustering," IEEE International Conference on Fuzzy Systems, pp. 1081-1085, 2002.
  11. S. Y. Kim, I. H. Ra, and S. H. Kim, "Design of wind turbine fault detection system based on performance curve," International Symposium on Advanced Intelligent System, vol. 13, pp. 2033-2036, 2012.
  12. A. Kusiak and W. Li, "Short-term prediction of wind power with a clustering approach," Renewable Energy, vol. 35, no. 10, pp. 2362-2369, 2010. https://doi.org/10.1016/j.renene.2010.03.027
  13. A. P. Leite and C. W. T. Borges, "Probabilistic wind farms generation model for reliability studies applied to brasilian site," IEEE Transactions on Power Systems, vol. 21, no. 4, pp. 1493-1501, 2006. https://doi.org/10.1109/TPWRS.2006.881160

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