LAPSE:2023.20706v1
Published Article

LAPSE:2023.20706v1
Observer-Based Robust Fault Predictive Control for Wind Turbine Time-Delay Systems with Sensor and Actuator Faults
March 20, 2023
Abstract
This paper presents a novel observer-based robust fault predictive control (OBRFPC) approach for a wind turbine time-delay system subject to constraints, actuator/sensor faults, and external disturbances. The proposed approach is based on an augmented state-space representation that contains state-space variables and estimation errors. The proposed augmented representation is then used to synthesize a robust predictive controller. In addition, an observer is developed and used to estimate both state variables and actuator/sensor faults. To ensure that the proposed approach has disturbance rejection capabilities, the disturbance estimates were merged with the prediction model. In addition, the disturbance rejection capabilities and fault tolerance were insured by formulating the control process as an optimization problem subject to constraints in terms of linear matrix inequalities (LMIs). As a result, the controller gains are acquired by solving an LMI problem to guarantee input-to-state stability in the presence of sensor and actuator faults. A simulation example is conducted on a nonlinear wind turbine (1 MW) model with 3 blades, a horizontal axis, and upwind variable speed subject to actuator/sensor faults in the pitch system. The results demonstrate the ability of the proposed method in dealing with nonlinear systems subject to external disturbances and keeping the control performance acceptable in the presence of actuator/sensor faults.
This paper presents a novel observer-based robust fault predictive control (OBRFPC) approach for a wind turbine time-delay system subject to constraints, actuator/sensor faults, and external disturbances. The proposed approach is based on an augmented state-space representation that contains state-space variables and estimation errors. The proposed augmented representation is then used to synthesize a robust predictive controller. In addition, an observer is developed and used to estimate both state variables and actuator/sensor faults. To ensure that the proposed approach has disturbance rejection capabilities, the disturbance estimates were merged with the prediction model. In addition, the disturbance rejection capabilities and fault tolerance were insured by formulating the control process as an optimization problem subject to constraints in terms of linear matrix inequalities (LMIs). As a result, the controller gains are acquired by solving an LMI problem to guarantee input-to-state stability in the presence of sensor and actuator faults. A simulation example is conducted on a nonlinear wind turbine (1 MW) model with 3 blades, a horizontal axis, and upwind variable speed subject to actuator/sensor faults in the pitch system. The results demonstrate the ability of the proposed method in dealing with nonlinear systems subject to external disturbances and keeping the control performance acceptable in the presence of actuator/sensor faults.
Record ID
Keywords
fault-tolerant control, linear matrix inequalities (LMIs), observer-based control, robust model predictive control, sensor and actuator faults, wind turbine model
Subject
Suggested Citation
Bououden S, Allouani F, Abboudi A, Chadli M, Boulkaibet I, Barakeh ZA, Neji B, Ghandour R. Observer-Based Robust Fault Predictive Control for Wind Turbine Time-Delay Systems with Sensor and Actuator Faults. (2023). LAPSE:2023.20706v1
Author Affiliations
Bououden S: Laboratory of SATIT, Department of Industrial Engineering, Abbes Laghrour University, Khenchela 40004, Algeria
Allouani F: Laboratory of SATIT, Department of Industrial Engineering, Abbes Laghrour University, Khenchela 40004, Algeria
Abboudi A: Department of Mechanical Engineering, Faculty of Sciences and Technology, Abbes Laghrour University, Khenchela 40004, Algeria
Chadli M: IBISC, Université Paris-Saclay, Univ Evry, Val d’Essonne, 91020 Evry, France
Boulkaibet I: College of Engineering and Technology, American University of the Middle East, Kuwait
Barakeh ZA: College of Engineering and Technology, American University of the Middle East, Kuwait [ORCID]
Neji B: College of Engineering and Technology, American University of the Middle East, Kuwait [ORCID]
Ghandour R: College of Engineering and Technology, American University of the Middle East, Kuwait
Allouani F: Laboratory of SATIT, Department of Industrial Engineering, Abbes Laghrour University, Khenchela 40004, Algeria
Abboudi A: Department of Mechanical Engineering, Faculty of Sciences and Technology, Abbes Laghrour University, Khenchela 40004, Algeria
Chadli M: IBISC, Université Paris-Saclay, Univ Evry, Val d’Essonne, 91020 Evry, France
Boulkaibet I: College of Engineering and Technology, American University of the Middle East, Kuwait
Barakeh ZA: College of Engineering and Technology, American University of the Middle East, Kuwait [ORCID]
Neji B: College of Engineering and Technology, American University of the Middle East, Kuwait [ORCID]
Ghandour R: College of Engineering and Technology, American University of the Middle East, Kuwait
Journal Name
Energies
Volume
16
Issue
2
First Page
858
Year
2023
Publication Date
2023-01-11
ISSN
1996-1073
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Original Submission
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PII: en16020858, Publication Type: Journal Article
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LAPSE:2023.20706v1
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https://doi.org/10.3390/en16020858
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