IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v204y2023icp300-312.html
   My bibliography  Save this article

Wind inflow observation from load harmonics via neural networks: A simulation and field study

Author

Listed:
  • Kim, Kwang-Ho
  • Bertelè, Marta
  • Bottasso, Carlo L.

Abstract

In this work, feed-forward neural networks are used to estimate the inflow of a wind turbine in real time during operation. A previously published method achieved this capability by correlating the turbine response to the wind inflow via a piecewise-linear model. Specifically, the existing formulation used the once-per-revolution harmonics of both in- and out-of-plane blade bending moments to estimate four wind parameters: the vertical and horizontal shears, and the vertical and horizontal misalignment angles. The novel method presented here builds on the same concept, but replaces the linear model with neural networks. Compared to the previous approach, the neural-based one is simpler to tune and use, because it decouples the wind parameters, which can be observed each one independently from the others. The proposed formulation is tested using both aeroservoelastic simulations and field data obtained on a 3.5 MW machine. In the field, independent measurements of the inflow are provided by a nearby hub-tall met mast, providing data for the training and verification of the wind observer. Notwithstanding some limitations of the experimental setup, results indicate that the neural networks produce estimates of the shears and yaw misalignment angle with accuracies compatible with typical use cases in wind turbine and wind farm control. Additionally, the neural-based observer appears to slightly outperform the original formulation in the lower wind speed range, where stronger nonlinearities in the load-wind mapping can occur.

Suggested Citation

  • Kim, Kwang-Ho & Bertelè, Marta & Bottasso, Carlo L., 2023. "Wind inflow observation from load harmonics via neural networks: A simulation and field study," Renewable Energy, Elsevier, vol. 204(C), pages 300-312.
    • Handle: RePEc:eee:renene:v:204:y:2023:i:c:p:300-312
    DOI: 10.1016/j.renene.2022.12.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122018365
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.12.051?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. Bottasso, C.L. & Riboldi, C.E.D., 2015. "Validation of a wind misalignment observer using field test data," Renewable Energy, Elsevier, vol. 74(C), pages 298-306.
    2. Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.
    3. Bottasso, C.L. & Croce, A. & Nam, Y. & Riboldi, C.E.D., 2012. "Power curve tracking in the presence of a tip speed constraint," Renewable Energy, Elsevier, vol. 40(1), pages 1-12.
    4. Bottasso, C.L. & Riboldi, C.E.D., 2014. "Estimation of wind misalignment and vertical shear from blade loads," Renewable Energy, Elsevier, vol. 62(C), pages 293-302.
    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. Yi Deng & Lei Wang & Yitong Li & Hai Liu & Yifei Wang, 2024. "EhdNet: Efficient Harmonic Detection Network for All-Phase Processing with Channel Attention Mechanism," Energies, MDPI, vol. 17(2), pages 1-20, January.

    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. Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.
    2. Kwansu Kim & Hyun-Gyu Kim & Yuan Song & Insu Paek, 2019. "Design and Simulation of an LQR-PI Control Algorithm for Medium Wind Turbine," Energies, MDPI, vol. 12(12), pages 1-18, June.
    3. Bottasso, C.L. & Croce, A. & Riboldi, C.E.D. & Nam, Y., 2013. "Multi-layer control architecture for the reduction of deterministic and non-deterministic loads on wind turbines," Renewable Energy, Elsevier, vol. 51(C), pages 159-169.
    4. Lio, Wai Hou & Larsen, Gunner Chr. & Thorsen, Gunhild R., 2021. "Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation," Renewable Energy, Elsevier, vol. 172(C), pages 1073-1086.
    5. Mehlan, Felix C. & Nejad, Amir R., 2023. "Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring," Renewable Energy, Elsevier, vol. 212(C), pages 70-81.
    6. Bottasso, C.L. & Riboldi, C.E.D., 2015. "Validation of a wind misalignment observer using field test data," Renewable Energy, Elsevier, vol. 74(C), pages 298-306.
    7. Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
    8. Bottasso, C.L. & Riboldi, C.E.D., 2014. "Estimation of wind misalignment and vertical shear from blade loads," Renewable Energy, Elsevier, vol. 62(C), pages 293-302.
    9. Keflemariam, Yisehak A. & Lee, Sang, 2023. "Control and dynamic analysis of a 10 MW floating wind turbine on a TetraSpar multi-body platform," Renewable Energy, Elsevier, vol. 217(C).

    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:204:y:2023:i:c:p:300-312. 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.