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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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 8:1109–1118
Sora T, Koivo HN (1991) Application of artificial neural networks in process fault diagnosis. In: Proceedings of SAFEPROCESS’91, vol 2, pp 133–138
Scarf PA (2007) A framework for condition monitoring and condition based maintenance. Qual Technol Quant Manag 4(2):301–312
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 591–594
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: EWEC2009
Gorinevsky D, Dittmar K, Mylaraswamy D, Nwadiogbu E (2002) Model-based diagnostics for an aircraft auxiliary power unit. In: IEEE conference on control applications
Lapira E, Brisset D, Ardakani HD, Siegel D, Lee J (2012) Wind turbine performance assessment using multi-regime modeling approach. Renew Energy 45:86–95
Yam RCM, Tse PW, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manufact Technol 17:383–391
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 57:552–568
Lange M, Focken U (2006) Physical approach to short-term wind power prediction. Springer, Berlin
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 21:273–284
Jang JSR (1993) ANFIS: adaptive-network based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Elijorde, F.I., Moon, D., Ahn, S., Kim, S., Lee, J. (2013). Wind Turbine Performance Monitoring Based on Hybrid Clustering Method. In: Jung, HK., Kim, J., Sahama, T., Yang, CH. (eds) Future Information Communication Technology and Applications. Lecture Notes in Electrical Engineering, vol 235. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6516-0_35
Download citation
DOI: https://doi.org/10.1007/978-94-007-6516-0_35
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-6515-3
Online ISBN: 978-94-007-6516-0
eBook Packages: EngineeringEngineering (R0)