The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Stable Adaptive On-line Neural Control for Wind Energy Conversion System

풍력 발전 계통의 적응 신경망 제어기 설계

  • 박장현 (목포대 공대 제어로봇공학과) ;
  • 김성환 (목포대 공대 제어로봇공학과) ;
  • 장영학 (목포대 공대 제어로봇공학과)
  • Received : 2011.03.09
  • Accepted : 2011.03.24
  • Published : 2011.04.01
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Abstract

This paper proposes an online adaptive neuro-controller for a wind energy conversion system (WECS) that is a highly nonlinear system intrinsically. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing neural network in the controller design in this paper. The proposed adaptive neural control scheme using radial-basis function network (RBFN) needs no system parameters to meet control objectives. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.

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References

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