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, 15 (12). pp. 6302-6312. ISSN 1551-3203

Preview

Text (Full text) Rahimilarki et al - Robust neural network fault estimation approach for nonlinear dynamic systems with applications to wind turbine systems AAM.pdf - Accepted Version Download (3MB) | Preview

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.

Actions (login required)

View Item

Downloads