IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1411-d749908.html
   My bibliography  Save this article

Generalized Sliding Mode Observers for Simultaneous Fault Reconstruction in the Presence of Uncertainty and Disturbance

Author

Listed:
  • Ashkan Taherkhani

    (Department of Electrical Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

  • Farhad Bayat

    (Department of Electrical Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

  • Kaveh Hooshmandi

    (Department of Electrical Engineering, Arak University of Technology, Arak 38181-46763, Iran)

  • Andrzej Bartoszewicz

    (Institute of Automatic Control, Lodz University of Technology, 90924 Lodz, Poland)

Abstract

In this paper, a generalized sliding mode observer design method is proposed for the robust reconstruction of sensors and actuators faults in the presence of both unknown disturbances and uncertainties. For this purpose, the effect of uncertainty and disturbance on the system has been considered in generalized state-space form, and the LMI tool is combined with the concept of an equivalent output error injection method to reduce the effects of them on the reconstruction process. The upper bound of the disturbance and uncertainty are minimized in the design of the sliding motion so that the reconstruction of the faults will be minimized. The design method is applied for actuator faults in the generalized state-space form, and then with some suitable filtering, the method extends as sensors and actuators coincidentally faults. Since in the proposed approach, the state trajectories do not leave the sliding manifold even in simultaneous sensors and actuators faults, then the faults are reconstructed based upon information retrieved from the equivalent output error injection signal. Due to the importance of the robust fault reconstruction in the wind energy conversion system (WECS), the proposed approach is successfully applied to a 5 MW wind turbine system. The simulation results verify the robust performances of the proposed approach in the presence of unknown perturbations and uncertainties.

Suggested Citation

  • Ashkan Taherkhani & Farhad Bayat & Kaveh Hooshmandi & Andrzej Bartoszewicz, 2022. "Generalized Sliding Mode Observers for Simultaneous Fault Reconstruction in the Presence of Uncertainty and Disturbance," Energies, MDPI, vol. 15(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1411-:d:749908
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1411/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1411/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. Guzman & F.-R. López-Estrada & V. Estrada-Manzo & G. Valencia-Palomo, 2021. "Actuator fault estimation based on a proportional-integral observer with nonquadratic Lyapunov functions," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(9), pages 1938-1951, July.
    2. Yang, Chunzhen & Liu, Jingquan & Zeng, Yuyun & Xie, Guangyao, 2019. "Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model," Renewable Energy, Elsevier, vol. 133(C), pages 433-441.
    3. Tan Van Nguyen & Cheolkeun Ha, 2019. "Experimental Study of Sensor Fault-Tolerant Control for an Electro-Hydraulic Actuator Based on a Robust Nonlinear Observer," Energies, MDPI, vol. 12(22), pages 1-22, November.
    4. Guodong You & Tao Xu & Honglin Su & Xiaoxin Hou & Jisheng Li, 2019. "Fault-Tolerant Control for Actuator Faults of Wind Energy Conversion System," Energies, MDPI, vol. 12(12), pages 1-16, June.
    5. Qinyue Zhu & Zhaoyang Li & Xitang Tan & Dabo Xie & Wei Dai, 2019. "Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer," Energies, MDPI, vol. 12(9), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    2. Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
    3. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    4. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.
    5. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
    6. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    7. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    8. Yingjie Zhang & Wentao Yan, 2023. "Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2557-2580, August.
    9. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    10. Yang, Xilian & Zhao, Qunfei & Wang, Yuzhang & Cheng, Kanru, 2023. "Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation," Energy, Elsevier, vol. 262(PA).
    11. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    12. Pang, Yanhua & He, Qun & Jiang, Guoqian & Xie, Ping, 2020. "Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 161(C), pages 510-524.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1411-:d:749908. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.