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Robust fault tolerant control of DFIG wind energy systems with unknown inputs

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  • Kamal, E.
  • Aitouche, A.

Abstract

This paper proposes a Fuzzy Dedicated Observers (FDOS) method using a Nonlinear Unknown Input Fuzzy Observer (UIFO) with a Fuzzy Scheduler Fault Tolerant Control (FSFTC) algorithm for fuzzy Takagi-Sugeno (TS) systems subject to sensor faults, parametric uncertainties, and time varying unknown inputs. FDOS provide residuals for detection and isolation of sensor faults which can affect a TS model. The TS fuzzy model is adopted for fuzzy modeling of the uncertain nonlinear system and establishing fuzzy state observers. The concept of Parallel Distributed Compensation (PDC) is employed to design FSFTC and fuzzy observers from the TS fuzzy models. TS fuzzy systems are classified into three families based on the input matrices and a FSFTC synthesis procedure is given for each family. In each family, sufficient conditions are derived for robust stabilization, in the sense of Taylor series stability and Lyapunov method, for the TS fuzzy system with parametric uncertainties, sensor faults, and unknown inputs. The sufficient conditions are formulated in the format of Linear Matrix Inequalities (LMIs). The effectiveness of the proposed controller design methodology is finally demonstrated through a wind energy system with Doubly Fed Induction Generators (DFIG) to illustrate the effectiveness of the proposed method.

Suggested Citation

  • Kamal, E. & Aitouche, A., 2013. "Robust fault tolerant control of DFIG wind energy systems with unknown inputs," Renewable Energy, Elsevier, vol. 56(C), pages 2-15.
    • Handle: RePEc:eee:renene:v:56:y:2013:i:c:p:2-15
    DOI: 10.1016/j.renene.2012.10.024
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    References listed on IDEAS

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    1. Gálvez-Carrillo, Manuel & Kinnaert, Michel, 2011. "Sensor fault detection and isolation in doubly-fed induction generators accounting for parameter variations," Renewable Energy, Elsevier, vol. 36(5), pages 1447-1457.
    2. Kalantar, M. & Mousavi G., S.M., 2010. "Dynamic behavior of a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage," Applied Energy, Elsevier, vol. 87(10), pages 3051-3064, October.
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    Cited by:

    1. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    2. Shi, Fengming & Patton, Ron, 2015. "An active fault tolerant control approach to an offshore wind turbine model," Renewable Energy, Elsevier, vol. 75(C), pages 788-798.
    3. Dhibi, Khaled & Mansouri, Majdi & Bouzrara, Kais & Nounou, Hazem & Nounou, Mohamed, 2022. "Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 194(C), pages 778-787.
    4. Xu Wang & Yanxia Shen, 2019. "Fault Tolerant Control of DFIG-Based Wind Energy Conversion System Using Augmented Observer," Energies, MDPI, vol. 12(4), pages 1-12, February.
    5. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    6. 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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