Advertisement

Wind Turbine Performance Monitoring Based on Hybrid Clustering Method

  • Frank I. Elijorde
  • Daesun Moon
  • Seokil Ahn
  • Sungho Kim
  • Jaewan Lee
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 235)

Abstract

Due to the largely increasing demand for electrical power, other sources of energy have to be sought and wind power is one of those. Wind farms from around the world have continued to thrive due to its cost-effectiveness and benefits. However, an utmost concern for wind farm operators is to keep the turbines in good working conditions in order to produce power at the most optimal level. For wind turbines, a maintenance activity can be very costly; therefore, it should be carried out from a well-guided decision. An accurate monitoring of a turbine’s performance is instrumental for detecting a potentially deteriorating state. In this paper, we present a performance monitoring system for wind turbines based on ANFIS, a hybrid neuro-fuzzy system. By taking advantage of the combined strengths of neural networks and fuzzy inference systems, an accurate modeling of wind turbine performance is established. Its performance is evaluated using actual SCADA and it proves to be a favorable alternative to conventional modeling techniques.

Keywords

Condition monitoring system Wind turbine system SCADA Clustering Artificial intelligence ANFIS 

Notes

Acknowledgments

This research was financially supported by the Ministry of Education, Science Technology (MEST) and National Research Foundation of Korea (NRF) through the Human Training Project for Regional innovation.

References

  1. 1.
    Calderaro V, Galdi V, Piccolo A, Siano P (2008) A fuzzy controller for maximum energy extraction from variable speed wind power generation systems. Electr Power Syst Res 8:1109–1118CrossRefGoogle Scholar
  2. 2.
    Sora T, Koivo HN (1991) Application of artificial neural networks in process fault diagnosis. In: Proceedings of SAFEPROCESS’91, vol 2, pp 133–138Google Scholar
  3. 3.
    Scarf PA (2007) A framework for condition monitoring and condition based maintenance. Qual Technol Quant Manag 4(2):301–312MathSciNetGoogle Scholar
  4. 4.
    Wilkinson MR, Tavner PJ (2004) Extracting condition monitoring information from a wind turbine drive train, UPEC 2004. In: 39th international universities power engineering conference, vol 2, pp 591–594Google Scholar
  5. 5.
    Lekou DJ, Mouzakis F, Anastasopoulo AA, Kourosis D (2009) Fused acoustic emission and vibration techniques for health monitoring of wind turbine gearboxes and bearings. In: EWEC2009Google Scholar
  6. 6.
    Gorinevsky D, Dittmar K, Mylaraswamy D, Nwadiogbu E (2002) Model-based diagnostics for an aircraft auxiliary power unit. In: IEEE conference on control applicationsGoogle Scholar
  7. 7.
    Lapira E, Brisset D, Ardakani HD, Siegel D, Lee J (2012) Wind turbine performance assessment using multi-regime modeling approach. Renew Energy 45:86–95CrossRefGoogle Scholar
  8. 8.
    Yam RCM, Tse PW, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manufact Technol 17:383–391CrossRefGoogle Scholar
  9. 9.
    Garcia MC, Sanz-Bobi MA, del Pico J (2006) SIMAP: intelligent system for predictive maintenance application to the health condition monitoring of a wind turbine gearbox. Comput Ind 57:552–568CrossRefGoogle Scholar
  10. 10.
    Lange M, Focken U (2006) Physical approach to short-term wind power prediction. Springer, BerlinGoogle Scholar
  11. 11.
    Barbounis TG, Theocharis JB, Alexiadis MC, Dokopoulos PS (2006) Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans Energy Convers 21:273–284Google Scholar
  12. 12.
    Jang JSR (1993) ANFIS: adaptive-network based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Frank I. Elijorde
    • 1
  • Daesun Moon
    • 2
  • Seokil Ahn
    • 2
  • Sungho Kim
    • 2
  • Jaewan Lee
    • 1
  1. 1.Department of Information and Communication EngineeringKunsan National UniversityGunsanSouth Korea
  2. 2.Department of Control and Robotics EngineeringKunsan National UniversityGunsanSouth Korea

Personalised recommendations