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Wind Turbine Tower Vibration Modeling and Monitoring by the Nonlinear State Estimation Technique (NSET)

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  • Peng Guo

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • David Infield

    (Institute for Energy and Environment, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK)

Abstract

With appropriate vibration modeling and analysis the incipient failure of key components such as the tower, drive train and rotor of a large wind turbine can be detected. In this paper, the Nonlinear State Estimation Technique (NSET) has been applied to model turbine tower vibration to good effect, providing an understanding of the tower vibration dynamic characteristics and the main factors influencing these. The developed tower vibration model comprises two different parts: a sub-model used for below rated wind speed; and another for above rated wind speed. Supervisory control and data acquisition system (SCADA) data from a single wind turbine collected from March to April 2006 is used in the modeling. Model validation has been subsequently undertaken and is presented. This research has demonstrated the effectiveness of the NSET approach to tower vibration; in particular its conceptual simplicity, clear physical interpretation and high accuracy. The developed and validated tower vibration model was then used to successfully detect blade angle asymmetry that is a common fault that should be remedied promptly to improve turbine performance and limit fatigue damage. The work also shows that condition monitoring is improved significantly if the information from the vibration signals is complemented by analysis of other relevant SCADA data such as power performance, wind speed, and rotor loads.

Suggested Citation

  • Peng Guo & David Infield, 2012. "Wind Turbine Tower Vibration Modeling and Monitoring by the Nonlinear State Estimation Technique (NSET)," Energies, MDPI, vol. 5(12), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:12:p:5279-5293:d:22218
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    References listed on IDEAS

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    1. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    2. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    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. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    3. Zhu, Zimo & Zhang, Jian & Zhu, Songye & Yang, Jun, 2023. "Digital twin technology for wind turbine towers based on joint load–response estimation: A laboratory experimental study," Applied Energy, Elsevier, vol. 352(C).
    4. Jakub Matoušek & Jindřich Duník & Ondřej Straka, 2020. "Density Difference Grid Design in a Point-Mass Filter," Energies, MDPI, vol. 13(16), pages 1-17, August.
    5. Wymore, Mathew L. & Van Dam, Jeremy E. & Ceylan, Halil & Qiao, Daji, 2015. "A survey of health monitoring systems for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 976-990.
    6. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez & Víctor Alonso-Gómez, 2019. "Maintenance Models Applied to Wind Turbines. A Comprehensive Overview," Energies, MDPI, vol. 12(2), pages 1-41, January.
    7. Yonglong Yan & Jian Li & David Wenzhong Gao, 2014. "Condition Parameter Modeling for Anomaly Detection in Wind Turbines," Energies, MDPI, vol. 7(5), pages 1-17, May.
    8. Zhengnan Hou & Xiaoxiao Lv & Shengxian Zhuang, 2021. "Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects," Energies, MDPI, vol. 14(22), pages 1-15, November.
    9. Peng Qian & Xiandong Ma & Dahai Zhang, 2017. "Estimating Health Condition of the Wind Turbine Drivetrain System," Energies, MDPI, vol. 10(10), pages 1-19, October.
    10. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.

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