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Nonlinear system identification for model-based condition monitoring of wind turbines

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  • Cross, Philip
  • Ma, Xiandong

Abstract

This paper proposes a data driven model-based condition monitoring scheme that is applied to wind turbines. The scheme is based upon a non-linear data-based modelling approach in which the model parameters vary as functions of the system variables. The model structure and parameters are identified directly from the input and output data of the process. The proposed method is demonstrated with data obtained from a simulation of a grid-connected wind turbine where it is used to detect grid and power electronic faults. The method is evaluated further with SCADA data obtained from an operational wind farm where it is employed to identify gearbox and generator faults. In contrast to artificial intelligence methods, such as artificial neural network-based models, the method employed in this paper provides a parametrically efficient representation of non-linear processes. Consequently, it is relatively straightforward to implement the proposed model-based method on-line using a field-programmable gate array.

Suggested Citation

  • Cross, Philip & Ma, Xiandong, 2014. "Nonlinear system identification for model-based condition monitoring of wind turbines," Renewable Energy, Elsevier, vol. 71(C), pages 166-175.
    • Handle: RePEc:eee:renene:v:71:y:2014:i:c:p:166-175
    DOI: 10.1016/j.renene.2014.05.035
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    1. Hyeonmin Kim & Jung-Taek Kim & Jaehyuk Eoh & Dong-Won Lim, 2018. "Development of a Physics-Based Monitoring Algorithm Detecting CO 2 Ingress Accidents in a Sodium-Cooled Fast Reactor," Energies, MDPI, vol. 12(1), pages 1-15, December.
    2. Bi, Ran & Zhou, Chengke & Hepburn, Donald M., 2017. "Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves," Renewable Energy, Elsevier, vol. 105(C), pages 674-688.
    3. Chen, Junsheng & Li, Jian & Chen, Weigen & Wang, Youyuan & Jiang, Tianyan, 2020. "Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders," Renewable Energy, Elsevier, vol. 147(P1), pages 1469-1480.
    4. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    5. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    6. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
    7. Chan Roh, 2022. "Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators," Energies, MDPI, vol. 15(9), pages 1-18, April.
    8. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    9. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
    10. Mengnan Cao & Yingning Qiu & Yanhui Feng & Hao Wang & Dan Li, 2016. "Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data," Energies, MDPI, vol. 9(10), pages 1-18, October.
    11. Wang, Yifei & Ma, Xiandong & Joyce, Malcolm J., 2016. "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis," Renewable Energy, Elsevier, vol. 97(C), pages 444-456.
    12. Malik Braik & Alaa Sheta & Heba Al-Hiary & Sultan Aljahdali, 2023. "Enhanced cuckoo search algorithm for industrial winding process modeling," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1911-1940, April.
    13. Xingqi Hu & Wen Tan & Guolian Hou, 2023. "PIDD2 Control of Large Wind Turbines’ Pitch Angle," Energies, MDPI, vol. 16(13), pages 1-22, July.
    14. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    15. Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    16. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
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