Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 12, Pages 6302-6312Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2893845
Keywords
Wind turbines; Fault diagnosis; Neural networks; Mathematical model; Observers; Stability analysis; Artificial neural network (ANN); fault estimation; input-to-state stability; linear matrix inequality; robustness; wind turbine systems
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Funding
- National Natural Science Foundation of China [61673074]
- Alexander von Humboldt Foundation [GRO/1117303 STP]
- E&E Faculty, University of Northumbria
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In this paper, a robust fault estimation approach is proposed for multi-input and multioutput 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.
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