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Effective wind speed estimation study of the wind turbine based on deep learning

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  • Chen, Peng
  • Han, Dezhi

Abstract

Wind speed is the driver of wind turbines, and the precise estimate of that makes it possible to improve the control effects and the efficiency of energy production. This paper proposes a method of effective wind speed estimator that considers the variation in blade radius and reconstructs the mapping of the aerodynamic. First, the proposed method utilizes available data of the wind turbine to train two neural network models based on radial basis functions (RBF). The models estimate the effective radius and reconstruct the aerodynamic mapping surface, respectively. Then, on this basis, train a wind speed estimation model based on the Long Short-Term Memory (LSTM) neural network, which can effectively estimate the current wind speed in real-time and predict the wind speed of the next time step as the reference for the following estimation. In addition, the RBF model and LSTM model can be updated and improved adaptively based on new data to ensure the accuracy of an effective wind speed estimator. Finally, the proposed wind speed estimation method is compared with the existing methods. The experimental results show that the proposed method has strong anti-interference characteristics, and improves the effective wind speed estimation accuracy over 70% on average.

Suggested Citation

  • Chen, Peng & Han, Dezhi, 2022. "Effective wind speed estimation study of the wind turbine based on deep learning," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003942
    DOI: 10.1016/j.energy.2022.123491
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    References listed on IDEAS

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    2. Jiahuan Lin & Weijia Yuan & Zhipeng Hu & Zijun Huang & Zining Yan & Hengju Huang & Rongye Zheng, 2025. "Fuzzy PID Individual Pitch Control with Effective Wind Speed Estimation for Offshore Floating Wind Turbines," Energies, MDPI, vol. 18(18), pages 1-12, September.
    3. Yang, Han & Yuan, Weimin & Zhu, Weijun & Sun, Zhenye & Zhang, Yanru & Zhou, Yingjie, 2024. "Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology," Applied Energy, Elsevier, vol. 364(C).
    4. Wang, Lijin & Fan, Weipeng & Jiang, Guoqian & Xie, Ping, 2023. "An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms," Energy, Elsevier, vol. 284(C).
    5. Shang, Jingyi & Gao, Jinfeng & Jiang, Xin & Liu, Mingguang & Liu, Dunnan, 2023. "Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework," Energy, Elsevier, vol. 263(PF).
    6. Cuauhtemoc Acosta Lúa & Domenico Bianchi & Salvador Martín Baragaño & Mario Di Ferdinando & Stefano Di Gennaro, 2023. "Robust Nonlinear Control of a Wind Turbine with a Permanent Magnet Synchronous Generator," Energies, MDPI, vol. 16(18), pages 1-19, September.
    7. Chen, Peng & Han, Dezhi, 2023. "Reward adaptive wind power tracking control based on deep deterministic policy gradient," Applied Energy, Elsevier, vol. 348(C).

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