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Transformation algorithm of wind turbine blade moment signals for blade condition monitoring

Author

Listed:
  • Lee, Jae-Kyung
  • Park, Joon-Young
  • Oh, Ki-Yong
  • Ju, Seung-Hwan
  • Lee, Jun-Shin

Abstract

To simplify signal analysis on wind turbine blades and enable their efficient monitoring, this paper presents a novel method of transforming blade moment signals on a horizontal axis 3-blade wind turbine. Instead of processing 3-blade moment signals directly, the proposed algorithm transforms the three sinusoidal signals into two static signals relative to the center of blade rotation through vector synthesis and coordinate transformation, and eliminates frequency components due to blade rotation from the obtained signals. Moreover, as an alternative to a rotational sensor, a blade rotation angle estimator is introduced. Its effectiveness was confirmed through simulations and field tests on an actual wind turbine.

Suggested Citation

  • Lee, Jae-Kyung & Park, Joon-Young & Oh, Ki-Yong & Ju, Seung-Hwan & Lee, Jun-Shin, 2015. "Transformation algorithm of wind turbine blade moment signals for blade condition monitoring," Renewable Energy, Elsevier, vol. 79(C), pages 209-218.
    • Handle: RePEc:eee:renene:v:79:y:2015:i:c:p:209-218
    DOI: 10.1016/j.renene.2014.11.030
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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.
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    Cited by:

    1. Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
    2. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function," Renewable Energy, Elsevier, vol. 181(C), pages 59-70.
    3. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    4. Pinjia Zhang & Delong Lu, 2019. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines," Energies, MDPI, vol. 12(14), pages 1-22, July.
    5. Esu, O.O. & Lloyd, S.D. & Flint, J.A. & Watson, S.J., 2016. "Feasibility of a fully autonomous wireless monitoring system for a wind turbine blade," Renewable Energy, Elsevier, vol. 97(C), pages 89-96.
    6. Tao, Cheng & Tao, Tao & He, Shukai & Bai, Xinjian & Liu, Yongqian, 2024. "Wind turbine blade icing diagnosis using B-SMOTE-Bi-GRU and RFE combined with icing mechanism," Renewable Energy, Elsevier, vol. 221(C).
    7. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    8. de Almeida, L.A.L. & Filho, A.J. Sguarezi & Capovilla, C.E. & Casella, I.R.S. & Costa, F.F., 2016. "An impulsive noise filter applied in wireless control of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 347-353.
    9. Jiang, Zhiyu & Xing, Yihan, 2022. "Load mitigation method for wind turbines during emergency shutdowns," Renewable Energy, Elsevier, vol. 185(C), pages 978-995.

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