IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v220y2024ics0960148123015513.html
   My bibliography  Save this article

Exploring influence of air density deviation on power production of wind energy conversion system: Study on correction method

Author

Listed:
  • Jargalsaikhan, Nyam
  • Ueda, Soichiro
  • Masahiro, Furukakoi
  • Matayoshi, Hidehito
  • Mikhaylov, Alexey
  • Byambaa, Sergelen
  • Senjyu, Tomonobu

Abstract

Thousands of wind turbines are already installed, providing renewable energy to meet the growing energy demand of consumers. This article investigates the effect of air density deviation, which depends on weather conditions, on power production of operational wind turbines, and examines strategy to mitigate this effect. Throughout the year, the air density in the area where the wind turbines are installed experiences continuous variations. It affects the annual power production of the wind turbine. Therefore, it is essential to discover a method that can reduce the effect of air density changes on power production of the wind turbine without adding extra extender blade. The effect of air density deviation on parameter of the wind turbines is investigated using four years measurement data (with a 10-minute interval) as well as simulation data. To reduce air density effect, the pitch angle and torque gain are readjusted rather than setting it constant based on the air density value. The analysis and simulation study were conducted in Matlab/Simulink® software. The results revealed that the suggested correction in pitch angle and torque gain according air density effectively alleviated the effect of air density variations, and the power production of the wind turbine was increased.

Suggested Citation

  • Jargalsaikhan, Nyam & Ueda, Soichiro & Masahiro, Furukakoi & Matayoshi, Hidehito & Mikhaylov, Alexey & Byambaa, Sergelen & Senjyu, Tomonobu, 2024. "Exploring influence of air density deviation on power production of wind energy conversion system: Study on correction method," Renewable Energy, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:renene:v:220:y:2024:i:c:s0960148123015513
    DOI: 10.1016/j.renene.2023.119636
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123015513
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119636?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. Mu, Zhongqiu & Guo, Wenfeng & Li, Yan & Tagawa, Kotaro, 2023. "Wind tunnel test of ice accretion on blade airfoil for wind turbine under offshore atmospheric condition," Renewable Energy, Elsevier, vol. 209(C), pages 42-52.
    2. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
    3. Hosseini, Ehsan & Aghadavoodi, Ehsan & Fernández Ramírez, Luis M., 2020. "Improving response of wind turbines by pitch angle controller based on gain-scheduled recurrent ANFIS type 2 with passive reinforcement learning," Renewable Energy, Elsevier, vol. 157(C), pages 897-910.
    4. López-Queija, Javier & Robles, Eider & Jugo, Josu & Alonso-Quesada, Santiago, 2022. "Review of control technologies for floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Xie, Jingjie & Dong, Hongyang & Zhao, Xiaowei, 2023. "Data-driven torque and pitch control of wind turbines via reinforcement learning," Renewable Energy, Elsevier, vol. 215(C).
    6. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    7. Kosuke Takahashi & Nyam Jargalsaikhan & Shriram Rangarajan & Ashraf Mohamed Hemeida & Hiroshi Takahashi & Tomonobu Senjyu, 2020. "Output Control of Three-Axis PMSG Wind Turbine Considering Torsional Vibration Using H Infinity Control," Energies, MDPI, vol. 13(13), pages 1-13, July.
    8. Pryor, S.C. & Barthelmie, R.J., 2010. "Climate change impacts on wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 430-437, January.
    9. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    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. 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).
    2. Zhang, Zeyu & Liang, Yushi & Xue, Xinyue & Li, Yan & Zhang, Mulan & Li, Yiran & Ji, Xiaodong, 2024. "China's future wind energy considering air density during climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    3. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    4. Zeng, Xinmeng & Shao, Yanlin & Feng, Xingya & Xu, Kun & Jin, Ruijia & Li, Huajun, 2024. "Nonlinear hydrodynamics of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    5. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
    6. Alain Ulazia & Gabriel Ibarra-Berastegi, 2020. "Problem-Based Learning in University Studies on Renewable Energies: Case of a Laboratory Windpump," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    7. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    8. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    9. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    10. Jerez, S. & Thais, F. & Tobin, I. & Wild, M. & Colette, A. & Yiou, P. & Vautard, R., 2015. "The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1-15.
    11. Huo, Dongxia & Bagadeem, Salim & Elsherazy, Tarek Abbas & Nasnodkar, Siddhesh Prabhu & Kalra, Akash, 2023. "Renewable energy consumption and the rising effect of climate policy uncertainty: Fresh policy analysis from China," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1459-1474.
    12. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    13. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
    14. Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    15. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    16. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    17. Rusu, Eugen, 2024. "The expected wind power dynamics in the Mediterranean Sea considering different climate change scenarios," Renewable Energy, Elsevier, vol. 227(C).
    18. Pei Zhang & Shugeng Yang & Yan Li & Jiayang Gu & Zhiqiang Hu & Ruoyu Zhang & Yougang Tang, 2020. "Dynamic Response of Articulated Offshore Wind Turbines under Different Water Depths," Energies, MDPI, vol. 13(11), pages 1-20, June.
    19. Palanimuthu, Kumarasamy & Joo, Young Hoon, 2023. "Reliability improvement of the large-scale wind turbines with actuator faults using a robust fault-tolerant synergetic pitch control," Renewable Energy, Elsevier, vol. 217(C).
    20. Perera, A.T.D. & Khayatian, F. & Eggimann, S. & Orehounig, K. & Halgamuge, Saman, 2022. "Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs," Applied Energy, Elsevier, vol. 328(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:220:y:2024:i:c:s0960148123015513. 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.