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Neural-Network Based Robust FTC: Application to Wind Turbines

  • Marcel Luzar
  • Marcin Witczak
  • Józef Korbicz
  • Piotr Witczak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8467)

Abstract

The paper deals with the problem of a robust fault diagnosis of a wind turbine. The preliminary part of the paper describes the Linear Parameter-Varying model derivation with a Recurrent Neural Network. The subsequent part of the paper describes a robust fault detection, isolation and identification scheme, which is based on the observer and \(\mathcal{H}_{\infty}\) framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error while guaranteeing the convergence of the observer. Moreover, the controller parameters selection method of the considered system is presented. Final part of the paper shows the experimental results regarding wind turbines, which confirms the effectiveness of proposed approach.

Keywords

Fault diagnosis fault identification robust control fault-tolerant control neural networks 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marcel Luzar
    • 1
  • Marcin Witczak
    • 1
  • Józef Korbicz
    • 1
  • Piotr Witczak
    • 1
  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

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