Actuator FDI Scheme for a Wind Turbine Benchmark Using Sliding Mode Observers

This paper proposes a fault detection and isolation (FDI) scheme for a wind turbines subject to actuator faults in both the pitch system and the drive train system. The proposed scheme addresses fault detection and isolation problems using a fault estimation approach. The proposed approach considers...

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Bibliographic Details
Published in:Processes 2023-06, Vol.11 (6), p.1690
Main Authors: Borja-Jaimes, Vicente, Adam-Medina, Manuel, García-Morales, Jarniel, Guerrero-Ramírez, Gerardo Vicente, López-Zapata, Betty Yolanda, Sánchez-Coronado, Eduardo Mael
Format: Article
Language:English
Subjects:
Actuators
Air-turbines
Analysis
Benchmarks
Coordinate transformations
Error analysis
Fault detection
Faults
Foreign investments
Kalman filters
Mathematical models
Methods
Monitoring systems
Neural networks
Observers
Parameter estimation
Powertrain
Sensors
Turbines
Wind power
Wind turbines
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Summary:This paper proposes a fault detection and isolation (FDI) scheme for a wind turbines subject to actuator faults in both the pitch system and the drive train system. The proposed scheme addresses fault detection and isolation problems using a fault estimation approach. The proposed approach considers the use of a particular class of sliding mode observers (SMOs) designed to maintain the sliding motion even in the presence of actuator faults. The fault detection problem is solved by reconstructing the actuator faults through an appropriate analysis of the nonlinear output error injection signal, which is required to keep the SMO in a sliding motion. To ensure accurate fault reconstruction, only two conditions are required, namely that the faults are bounded and they meet the matching condition. A scheme based on a bank of SMOs is proposed to solve the fault detection and isolation problem in the pitch system. For the drive train system, a scheme using only one SMO is proposed. The performance of the proposed FDI scheme is validated by using a wind turbine benchmark model subjected to several actuator faults. Normalized root mean square error (NRMSE) analysis is performed to evaluate the accuracy of the actuator fault estimations.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr11061690