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.
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Luzar, M., Witczak, M., Korbicz, J., Witczak, P. (2014). Neural-Network Based Robust FTC: Application to Wind Turbines. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_10
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DOI: https://doi.org/10.1007/978-3-319-07173-2_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07172-5
Online ISBN: 978-3-319-07173-2
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