Wind Turbine Performance Monitoring Based on Hybrid Clustering Method

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


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.


Condition monitoring system Wind turbine system SCADA Clustering Artificial intelligence ANFIS 



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.


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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

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