IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v278y2023ipas0360544223013348.html
   My bibliography  Save this article

Machine learning based surrogate model to analyze wind tunnel experiment data of Darrieus wind turbines

Author

Listed:
  • Cheng, Biyi
  • Yao, Yingxue

Abstract

Wind tunnel experiment is one of the most common research methods of wind turbines. However, the processing of experiment data towards Vertical Axis Wind Turbines (VAWT) are inefficient. In this work, a data-driven surrogated model based on Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Decision Tree Regression (DTR) is proposed to regress the experimental data of aerodynamic forces. Meanwhile, a novel U-typed Darrieus Wind Turbine (UDWT) and two H-typed VAWTs are manufactured in this work. A measurement system combining the magnetic remanence brake, six force sensor, and data processing subsystem is assembled to investigate the aerodynamic forces of wind turbines. A Spin-Down measurement procedure is adopted to collect experiment data in various operating conditions. Results showed that SVM- and GPR-based regression models feature the better fitting ability with R2 larger than 0.99, which can regress the experiment data accurately. ANN-based surrogated model can reproduce the fluctuation of experiment data because of the best learning ability. Aerodynamic forces of UDWT outperform H-type wind turbines at azimuth angle of 90° and 270°.

Suggested Citation

  • Cheng, Biyi & Yao, Yingxue, 2023. "Machine learning based surrogate model to analyze wind tunnel experiment data of Darrieus wind turbines," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223013348
    DOI: 10.1016/j.energy.2023.127940
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223013348
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127940?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. Li, Qing'an & Maeda, Takao & Kamada, Yasunari & Murata, Junsuke & Furukawa, Kazuma & Yamamoto, Masayuki, 2015. "Effect of number of blades on aerodynamic forces on a straight-bladed Vertical Axis Wind Turbine," Energy, Elsevier, vol. 90(P1), pages 784-795.
    2. Howell, Robert & Qin, Ning & Edwards, Jonathan & Durrani, Naveed, 2010. "Wind tunnel and numerical study of a small vertical axis wind turbine," Renewable Energy, Elsevier, vol. 35(2), pages 412-422.
    3. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    4. Battisti, L. & Persico, G. & Dossena, V. & Paradiso, B. & Raciti Castelli, M. & Brighenti, A. & Benini, E., 2018. "Experimental benchmark data for H-shaped and troposkien VAWT architectures," Renewable Energy, Elsevier, vol. 125(C), pages 425-444.
    5. Li, Qing’an & Maeda, Takao & Kamada, Yasunari & Murata, Junsuke & Shimizu, Kento & Ogasawara, Tatsuhiko & Nakai, Alisa & Kasuya, Takuji, 2016. "Effect of solidity on aerodynamic forces around straight-bladed vertical axis wind turbine by wind tunnel experiments (depending on number of blades)," Renewable Energy, Elsevier, vol. 96(PA), pages 928-939.
    6. Danao, Louis Angelo & Eboibi, Okeoghene & Howell, Robert, 2013. "An experimental investigation into the influence of unsteady wind on the performance of a vertical axis wind turbine," Applied Energy, Elsevier, vol. 107(C), pages 403-411.
    7. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
    8. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    9. Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
    10. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
    11. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Power prediction formula for blade design and optimization of Dual Darrieus Wind Turbines based on Taguchi Method and Genetic Expression Programming model," Renewable Energy, Elsevier, vol. 192(C), pages 583-605.
    12. Mustafa Kaya, 2019. "A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    13. Jinkyoo Park & Soon-Duck Kwon & Kincho Law, 2017. "A Data-Driven, Cooperative Approach for Wind Farm Control: A Wind Tunnel Experimentation," Energies, MDPI, vol. 10(7), pages 1-17, June.
    14. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    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. Jiang, Xue & Xu, Chuanbao & Yang, Zhe & Ge, Chuntao & Nie, Shishuai & Yu, Anfeng & Ling, Xiaodong, 2024. "Equivalent notional nozzle model for high nozzle pressure ratio leakage of petrochemical high-pressure facility," Energy, Elsevier, vol. 295(C).
    2. Shen, Zhuang & Gong, Shuguang & Xie, Guilan & Lu, Haishan & Guo, Weiyu, 2024. "Investigation of the effect of critical structural parameters on the aerodynamic performance of the double darrieus vertical axis wind turbine," Energy, Elsevier, vol. 290(C).

    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. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
    2. Li, Qing'an & Maeda, Takao & Kamada, Yasunari & Shimizu, Kento & Ogasawara, Tatsuhiko & Nakai, Alisa & Kasuya, Takuji, 2017. "Effect of rotor aspect ratio and solidity on a straight-bladed vertical axis wind turbine in three-dimensional analysis by the panel method," Energy, Elsevier, vol. 121(C), pages 1-9.
    3. Li, Qing'an & Maeda, Takao & Kamada, Yasunari & Murata, Junsuke & Kawabata, Toshiaki & Shimizu, Kento & Ogasawara, Tatsuhiko & Nakai, Alisa & Kasuya, Takuji, 2016. "Wind tunnel and numerical study of a straight-bladed vertical axis wind turbine in three-dimensional analysis (Part I: For predicting aerodynamic loads and performance)," Energy, Elsevier, vol. 106(C), pages 443-452.
    4. Wang, Wei-Cheng & Wang, Jheng-Jie & Chong, Wen Tong, 2019. "The effects of unsteady wind on the performances of a newly developed cross-axis wind turbine: A wind tunnel study," Renewable Energy, Elsevier, vol. 131(C), pages 644-659.
    5. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    6. Peng, H.Y. & Lam, H.F. & Liu, H.J., 2019. "Power performance assessment of H-rotor vertical axis wind turbines with different aspect ratios in turbulent flows via experiments," Energy, Elsevier, vol. 173(C), pages 121-132.
    7. Li, Qingan & Cai, Chang & Maeda, Takao & Kamada, Yasunari & Shimizu, Kento & Dong, Yehong & Zhang, Fanghong & Xu, Jianzhong, 2021. "Visualization of aerodynamic forces and flow field on a straight-bladed vertical axis wind turbine by wind tunnel experiments and panel method," Energy, Elsevier, vol. 225(C).
    8. Lee, Kung-Yen & Tsao, Shao-Hua & Tzeng, Chieh-Wen & Lin, Huei-Jeng, 2018. "Influence of the vertical wind and wind direction on the power output of a small vertical-axis wind turbine installed on the rooftop of a building," Applied Energy, Elsevier, vol. 209(C), pages 383-391.
    9. Li, Qing'an & Maeda, Takao & Kamada, Yasunari & Ogasawara, Tatsuhiko & Nakai, Alisa & Kasuya, Takuji, 2017. "Investigation of power performance and wake on a straight-bladed vertical axis wind turbine with field experiments," Energy, Elsevier, vol. 141(C), pages 1113-1123.
    10. Wekesa, David Wafula & Wang, Cong & Wei, Yingjie & Danao, Louis Angelo M., 2017. "Analytical and numerical investigation of unsteady wind for enhanced energy capture in a fluctuating free-stream," Energy, Elsevier, vol. 121(C), pages 854-864.
    11. Kun Wang & Li Zou & Aimin Wang & Peidong Zhao & Yichen Jiang, 2020. "Wind Tunnel Study on Wake Instability of Twin H-Rotor Vertical-Axis Turbines," Energies, MDPI, vol. 13(17), pages 1-18, August.
    12. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    13. Hassan, Syed Saddam ul & Javaid, M. Tariq & Rauf, Umar & Nasir, Sheharyar & Shahzad, Aamer & Salamat, Shuaib, 2023. "Systematic investigation of power enhancement of Vertical Axis Wind Turbines using bio-inspired leading edge tubercles," Energy, Elsevier, vol. 270(C).
    14. Reddy, K. Bheemalingeswara & Bhosale, Amit C., 2024. "Effect of number of blades on performance and wake recovery for a vertical axis helical hydrokinetic turbine," Energy, Elsevier, vol. 299(C).
    15. Emejeamara, F.C. & Tomlin, A.S. & Millward-Hopkins, J.T., 2015. "Urban wind: Characterisation of useful gust and energy capture," Renewable Energy, Elsevier, vol. 81(C), pages 162-172.
    16. Hand, Brian & Kelly, Ger & Cashman, Andrew, 2021. "Aerodynamic design and performance parameters of a lift-type vertical axis wind turbine: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    17. Jin, Xin & Zhao, Gaoyuan & Gao, KeJun & Ju, Wenbin, 2015. "Darrieus vertical axis wind turbine: Basic research methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 212-225.
    18. Ma, Ning & Lei, Hang & Han, Zhaolong & Zhou, Dai & Bao, Yan & Zhang, Kai & Zhou, Lei & Chen, Caiyong, 2018. "Airfoil optimization to improve power performance of a high-solidity vertical axis wind turbine at a moderate tip speed ratio," Energy, Elsevier, vol. 150(C), pages 236-252.
    19. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    20. Lombardi, Lidia & Mendecka, Barbara & Carnevale, Ennio & Stanek, Wojciech, 2018. "Environmental impacts of electricity production of micro wind turbines with vertical axis," Renewable Energy, Elsevier, vol. 128(PB), pages 553-564.

    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:278:y:2023:i:pa:s0360544223013348. 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.