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Robust Neural Network Fault Estimation Approach for Nonlinear Dynamic Systems With Applications to Wind Turbine Systems
- Source :
- IEEE Transactions on Industrial Informatics. 15:6302-6312
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
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.
- Subjects :
- Wind power
Artificial neural network
H600
business.industry
Computer science
G400
020208 electrical & electronic engineering
Linear matrix inequality
02 engineering and technology
Turbine
Computer Science Applications
Control and Systems Engineering
Control theory
Robustness (computer science)
Stability theory
Nonlinear dynamic systems
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
business
Actuator
Information Systems
Subjects
Details
- ISSN :
- 19410050 and 15513203
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Industrial Informatics
- Accession number :
- edsair.doi.dedup.....60edf09f5757b81d6da06a00822b2d6f