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Detection and compensation of anomalous conditions in a wind turbine

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  • Hur, S.
  • Recalde-Camacho, L.
  • Leithead, W.E.

Abstract

Anomalies in the wind field and structural anomalies can cause unbalanced loads on the components and structure of a wind turbine. For example, large unbalanced rotor loads could arise from blades sweeping through low level jets resulting in wind shear, which is an example of anomaly. The lifespan of the blades could be increased if wind shear can be detected and appropriately compensated. The work presented in this paper proposes a novel anomaly detection and compensation scheme based on the Extended Kalman Filter. Simulation results are presented demonstrating that it can successfully be used to facilitate the early detection of various anomalous conditions, including wind shear, mass imbalance, aerodynamic imbalance and extreme gusts, and also that the wind turbine controllers can subsequently be modified to take appropriate diagnostic action to compensate for such anomalous conditions.

Suggested Citation

  • Hur, S. & Recalde-Camacho, L. & Leithead, W.E., 2017. "Detection and compensation of anomalous conditions in a wind turbine," Energy, Elsevier, vol. 124(C), pages 74-86.
    • Handle: RePEc:eee:energy:v:124:y:2017:i:c:p:74-86
    DOI: 10.1016/j.energy.2017.02.036
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    References listed on IDEAS

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    1. 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.
    2. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
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    Cited by:

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    2. Mucchielli, P. & Bhowmik, B. & Ghosh, B. & Pakrashi, V., 2021. "Real-time accurate detection of wind turbine downtime - An Irish perspective," Renewable Energy, Elsevier, vol. 179(C), pages 1969-1989.
    3. Fan Zhang & Juchuan Dai & Deshun Liu & Linxing Li & Xin Long, 2019. "Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data," Energies, MDPI, vol. 12(3), pages 1-20, February.
    4. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.
    5. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    6. Xing, Zuoxia & Chen, Mingyang & Cui, Jia & Chen, Zhe & Xu, Jian, 2022. "Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network," Renewable Energy, Elsevier, vol. 197(C), pages 1020-1033.
    7. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
    8. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.

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