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Uncertainty estimator-based dual layer adaptive fault-tolerant control for wind turbines

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  • Mazare, Mahmood
  • Taghizadeh, Mostafa

Abstract

Under the detrimental effects of sensor and actuator faults, the blade pitch system is found to be the least reliable subsystem. Therefore, to apprehend required level of reliability and efficiency, an efficient Fault Tolerant Control framework (FTC) seems crucial. This paper presents an Active FTC (AFTC) strategy to control the pitch angle of a wind turbine in the presence of actuator and sensor faults, uncertainties and exogenous disturbances. First, a lumped term consisting of model uncertainty and disturbance is estimated by a novel dual layer adaptive uncertainty estimator. Next, to achieve high accuracy in supplying the required power, a continuous adaptive time delay control is designed based on the estimated uncertainties. In the proposed controller, chattering caused by discontinuous control term is eliminated, and faults are accommodated. Stability of the closed-loop system is demonstrated by the Lyapunov theory. Furthermore, in order to verify the validity of the proposed strategy, the controller is implemented in FAST-MATLAB/Simulink for five different load cases generated using TurbSim. Results confirm the effectiveness and superiority of the proposed structure in the presence of sensor and actuator faults (bias, gain, performance degradation and actuator stuck) compared to Nonlinear PI (N-PI) control and Feedback Linearized Control (FLC) schemes.

Suggested Citation

  • Mazare, Mahmood & Taghizadeh, Mostafa, 2022. "Uncertainty estimator-based dual layer adaptive fault-tolerant control for wind turbines," Renewable Energy, Elsevier, vol. 188(C), pages 545-560.
  • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:545-560
    DOI: 10.1016/j.renene.2022.02.030
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    References listed on IDEAS

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    1. Jabbari Asl, Hamed & Yoon, Jungwon, 2016. "Power capture optimization of variable-speed wind turbines using an output feedback controller," Renewable Energy, Elsevier, vol. 86(C), pages 517-525.
    2. Eriksson, Sandra & Bernhoff, Hans & Leijon, Mats, 2008. "Evaluation of different turbine concepts for wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(5), pages 1419-1434, June.
    3. Yolanda Vidal & Christian Tutivén & José Rodellar & Leonardo Acho, 2015. "Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via a Discrete Time Controller with a Disturbance Compensator," Energies, MDPI, vol. 8(5), pages 1-17, May.
    4. Mazare, Mahmood & Taghizadeh, Mostafa & Ghaf-Ghanbari, Pegah, 2021. "Fault tolerant control of wind turbines with simultaneous actuator and sensor faults using adaptive time delay control," Renewable Energy, Elsevier, vol. 174(C), pages 86-101.
    5. Chen, Jian & Yao, Wei & Zhang, Chuan-Ke & Ren, Yaxing & Jiang, Lin, 2019. "Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control," Renewable Energy, Elsevier, vol. 134(C), pages 478-495.
    6. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    7. Yin, Xiu-xing & Lin, Yong-gang & Li, Wei & Gu, Ya-jing & Wang, Xiao-jun & Lei, Peng-fei, 2015. "Design, modeling and implementation of a novel pitch angle control system for wind turbine," Renewable Energy, Elsevier, vol. 81(C), pages 599-608.
    8. Lei Wang & Ming Cai & Hu Zhang & Fuad Alsaadi & Liu Chen, 2017. "Active Fault-Tolerant Control for Wind Turbine with Simultaneous Actuator and Sensor Faults," Complexity, Hindawi, vol. 2017, pages 1-11, December.
    9. Cho, Seongpil & Gao, Zhen & Moan, Torgeir, 2018. "Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 306-321.
    10. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
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