Robust neural network fault estimation approach for nonlinear dynamic systems with applications to wind turbine systems

Rahimilarki, Reihane, Gao, Zhiwei, Zhang, Aihua and Binns, Richard (2019) Robust neural network fault estimation approach for nonlinear dynamic systems with applications to wind turbine systems. IEEE Transactions on Industrial Informatics. ISSN 1551-3203 (In Press)

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Rahimilarki et al - Robust neural network fault estimation approach for nonlinear dynamic systems with applications to wind turbine systems AAM.pdf - Accepted Version

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Official URL: https://doi.org/10.1109/TII.2019.2893845

Abstract

In this paper, a robust fault estimation approach is proposed for multi-input and multi-output nonlinear dynamic systems on the basis of back propagation neural networks. The augmented system approach, input-to-state stability theory, linear matrix inequality optimization, and neural network training/learning are integrated so that a robust simultaneous estimate of system states and actuator faults are achieved. The proposed approaches are finally applied to a 4.8 MW wind turbine benchmark system, and the effectiveness is well demonstrated.

Item Type: Article
Uncontrolled Keywords: Artificial neural network, fault estimation, input to state stability, linear matrix inequality, robustness, wind turbine systems
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 16 Jan 2019 15:50
Last Modified: 12 Oct 2019 11:35
URI: http://nrl.northumbria.ac.uk/id/eprint/37607

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