IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v79y2015icp209-218.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114007459
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2014.11.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    7. 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.
    8. Jiang, Zhiyu & Xing, Yihan, 2022. "Load mitigation method for wind turbines during emergency shutdowns," Renewable Energy, Elsevier, vol. 185(C), pages 978-995.

    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. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
    2. Le Zhang & Qiang Yang, 2020. "Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox," Energies, MDPI, vol. 13(2), pages 1-13, January.
    3. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    4. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    5. Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    6. Taylor, Josh A. & Dhople, Sairaj V. & Callaway, Duncan S., 2016. "Power systems without fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1322-1336.
    7. Hare, James & Shi, Xiaofang & Gupta, Shalabh & Bazzi, Ali, 2016. "Fault diagnostics in smart micro-grids: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1114-1124.
    8. Wenyi, Liu & Zhenfeng, Wang & Jiguang, Han & Guangfeng, Wang, 2013. "Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM," Renewable Energy, Elsevier, vol. 50(C), pages 1-6.
    9. Harrold, Magnus & Ouro, Pablo & O’Doherty, Tim, 2020. "Performance assessment of a tidal turbine using two flow references," Renewable Energy, Elsevier, vol. 153(C), pages 624-633.
    10. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    11. 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.
    12. Guerine, A. & El Hami, A. & Walha, L. & Fakhfakh, T. & Haddar, M., 2017. "Dynamic response of wind turbine gear system with uncertain-but-bounded parameters using interval analysis method," Renewable Energy, Elsevier, vol. 113(C), pages 679-687.
    13. Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
    14. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    15. Yunxuan Dong & Jianzhou Wang & Chen Wang & Zhenhai Guo, 2017. "Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting," Energies, MDPI, vol. 10(4), pages 1-27, April.
    16. 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.
    17. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    18. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    19. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    20. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.

    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:eee:renene:v:79:y:2015:i:c:p:209-218. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.