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

Reihane Rahimilarki, Zhiwei Gao*, Aihua Zhang, Richard Binns

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
9 Downloads (Pure)

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.
Original languageEnglish
Article number8616801
Pages (from-to)6302-6312
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume15
Issue number12
Early online date17 Jan 2019
DOIs
Publication statusPublished - 5 Dec 2019

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