IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v293y2024ics0360544224003979.html
   My bibliography  Save this article

Multi-criteria optimal design of small wind turbine blades based on deep learning methods

Author

Listed:
  • Jordi, Zavala J.
  • Erasmo, Cadenas
  • Rafael, Campos-Amezcua

Abstract

The development of a Deep Learning (DL) model using multi-criteria optimal design of wind turbine blades is presented, focusing on the key variables TSR and Von Mises stress to predict: blade mass, power coefficient and natural frequency. The DL model was trained with data from three mathematical functions generated using distance-weighted inverse interpolation. The model allowed the generation of multiple feasible designs, which satisfy the design constraints. The BEM theory was used in the generation of the aerodynamic model of the 12.5 kW wind turbine using the NREL-S818 airfoil. Subsequently, the structural behavior of the blades was analyzed under three design load cases specified in IEC 61400-2. The results showed that in the training phase, the MAE, MSE and MSR error metrics were an essential guide in the development of the DL model. Interesting behaviors were observed due to the diverse results obtained, which are probably due to the multidimensional fits that are difficult to observe in the fit functions generated. However, training between 200 and 250 epochs performed better, with errors ranging from 0.05 to 0.120 for the MAE. The DL model exhibited the remarkable ability to predict the optimal output variables, with accuracy ranging from 90% to 98%.

Suggested Citation

  • Jordi, Zavala J. & Erasmo, Cadenas & Rafael, Campos-Amezcua, 2024. "Multi-criteria optimal design of small wind turbine blades based on deep learning methods," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003979
    DOI: 10.1016/j.energy.2024.130625
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224003979
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130625?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. Fatehi, Mostafa & Nili-Ahmadabadi, Mahdi & Nematollahi, Omid & Minaiean, Ali & Kim, Kyung Chun, 2019. "Aerodynamic performance improvement of wind turbine blade by cavity shape optimization," Renewable Energy, Elsevier, vol. 132(C), pages 773-785.
    2. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
    3. Wen, Hao & Sang, Song & Qiu, Chenhui & Du, Xiangrui & Zhu, Xiao & Shi, Qian, 2019. "A new optimization method of wind turbine airfoil performance based on Bessel equation and GABP artificial neural network," Energy, Elsevier, vol. 187(C).
    4. Zhu, Jie & Zhou, Zhong & Cai, Xin, 2020. "Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model," Renewable Energy, Elsevier, vol. 150(C), pages 523-537.
    Full references (including those not matched with items on IDEAS)

    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. Meng, Hang & Lien, Fue-Sang & Yee, Eugene & Shen, Jingfang, 2020. "Modelling of anisotropic beam for rotating composite wind turbine blade by using finite-difference time-domain (FDTD) method," Renewable Energy, Elsevier, vol. 162(C), pages 2361-2379.
    2. Escalera Mendoza, Alejandra S. & Griffith, D. Todd & Jeong, Michael & Qin, Chris & Loth, Eric & Phadnis, Mandar & Pao, Lucy & Selig, Michael S., 2023. "Aero-structural rapid screening of new design concepts for offshore wind turbines," Renewable Energy, Elsevier, vol. 219(P2).
    3. Dan Li & Hongbing Bao & Ning Zhao, 2023. "Research of Turbine Tower Optimization Based on Criterion Method," Energies, MDPI, vol. 16(2), pages 1-17, January.
    4. Li, Yunfeng & Xue, Wenli & Wu, Ting & Wang, Huaizhi & Zhou, Bin & Aziz, Saddam & He, Yang, 2021. "Intrusion detection of cyber physical energy system based on multivariate ensemble classification," Energy, Elsevier, vol. 218(C).
    5. Liu, Qingsong & Miao, Weipao & Ye, Qi & Li, Chun, 2022. "Performance assessment of an innovative Gurney flap for straight-bladed vertical axis wind turbine," Renewable Energy, Elsevier, vol. 185(C), pages 1124-1138.
    6. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    7. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    8. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
    9. Dominik Błoński & Katarzyna Strzelecka & Henryk Kudela, 2021. "Vortex Trapping Cavity on Airfoil: High-Order Penalized Vortex Method Numerical Simulation and Water Tunnel Experimental Investigation," Energies, MDPI, vol. 14(24), pages 1-32, December.
    10. Sang-Lae Lee & SangJoon Shin, 2020. "Wind Turbine Blade Optimal Design Considering Multi-Parameters and Response Surface Method," Energies, MDPI, vol. 13(7), pages 1-23, April.
    11. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    12. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    13. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    14. Navarkar, Abhishek & Hasti, Veeraraghava Raju & Deneke, Elihu & Gore, Jay P., 2020. "A data-driven model for thermodynamic properties of a steam generator under cycling operation," Energy, Elsevier, vol. 211(C).
    15. Ghadah Alkhayat & Syed Hamid Hasan & Rashid Mehmood, 2023. "A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate," Sustainability, MDPI, vol. 15(24), pages 1-39, December.
    16. Yin, He & Yang, Mao-sen & Lan, Hai & Hong, Ying-Yi & Guo, Dong & Jin, Feng, 2024. "A hybrid graph attention network based method for interval prediction of shipboard solar irradiation," Energy, Elsevier, vol. 298(C).
    17. Al-Sanad, Shaikha & Wang, Lin & Parol, Jafarali & Kolios, Athanasios, 2021. "Reliability-based design optimisation framework for wind turbine towers," Renewable Energy, Elsevier, vol. 167(C), pages 942-953.
    18. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    19. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    20. Tang, Xinzi & Yuan, Keren & Gu, Nengwei & Li, Pengcheng & Peng, Ruitao, 2022. "An interval quantification-based optimization approach for wind turbine airfoil under uncertainties," Energy, Elsevier, vol. 244(PA).

    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:energy:v:293:y:2024:i:c:s0360544224003979. 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/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.