IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v215y2023ics0960148123008649.html
   My bibliography  Save this article

Hybrid physical and data driven modeling for dynamic operation characteristic simulation of wind turbine

Author

Listed:
  • Yan, Jie
  • Nuertayi, Akejiang
  • Yan, Yamin
  • Liu, Shan
  • Liu, Yongqian

Abstract

Accurate simulation of wind turbines is the key to achieve efficient control of wind turbines and optimal scheduling of wind power systems. Most of the existing methods are physical simulation models, which have heavy computational burden, especially for large-scale wind turbines, and the simulation accuracy is poor under complex dynamic operating conditions. In response, this paper proposes a hybrid physical and data driven model for dynamic operation characteristic simulation of wind turbines. Firstly, a database of dynamic characteristics of wind turbine operation under various wind conditions is constructed, and then the sequence learning (Seq2Seq) model is used to mine the time series characteristics of wind turbine operation parameters. Combining with long short-term memory (LSTM) network, the mapping relationship between various operating parameter time series to establish. On this basis, an attention mechanism is added to the model to learn the dynamic response characteristics of rotor inertia to natural wind process. Through numerical example analysis, the method proposed in this paper can accurately excavate and learn the time sequence dynamic characteristics of wind turbine operating conditions and parameters under various wind conditions, thereby improving the simulation accuracy and calculation efficiency of wind turbine output power and load dynamic characteristics. Taking the actual wind turbine operation data as an example, the simulation accuracy of wind turbine output power reaches 99.8%. Compared with traditional methods, and the simulation accuracy of tower root load reaches 88.5%.

Suggested Citation

  • Yan, Jie & Nuertayi, Akejiang & Yan, Yamin & Liu, Shan & Liu, Yongqian, 2023. "Hybrid physical and data driven modeling for dynamic operation characteristic simulation of wind turbine," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008649
    DOI: 10.1016/j.renene.2023.118958
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123008649
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.118958?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. Choe, Do-Eun & Kim, Hyoung-Chul & Kim, Moo-Hyun, 2021. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades," Renewable Energy, Elsevier, vol. 174(C), pages 218-235.
    2. Johansen, Katinka, 2021. "Blowing in the wind: A brief history of wind energy and wind power technologies in Denmark," Energy Policy, Elsevier, vol. 152(C).
    3. van Ouwerkerk, Jonas & Hainsch, Karlo & Candas, Soner & Muschner, Christoph & Buchholz, Stefanie & Günther, Stephan & Huyskens, Hendrik & Berendes, Sarah & Löffler, Konstantin & Bußar, Christian & Tar, 2022. "Comparing open source power system models - A case study focusing on fundamental modeling parameters for the German energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
    5. Li, Zhiguo & Gao, Zhiying & Chen, Yongyan & Zhang, Liru & Wang, Jianwen, 2022. "A novel dynamic stall model based on Theodorsen theory and its application," Renewable Energy, Elsevier, vol. 193(C), pages 344-356.
    6. Ching-Sung Wang & Mao-Hsiung Chiang, 2016. "A Novel Pitch Control System of a Large Wind Turbine Using Two-Degree-of-Freedom Motion Control with Feedback Linearization Control," Energies, MDPI, vol. 9(10), pages 1-18, September.
    7. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    8. Kim, Bumsuk & Kim, Woojune & Lee, Sanglae & Bae, Sungyoul & Lee, Youngho, 2013. "Developement and verification of a performance based optimal design software for wind turbine blades," Renewable Energy, Elsevier, vol. 54(C), pages 166-172.
    9. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
    10. Xu, Zifei & Bashir, Musa & Yang, Yang & Wang, Xinyu & Wang, Jin & Ekere, Nduka & Li, Chun, 2022. "Multisensory collaborative damage diagnosis of a 10 MW floating offshore wind turbine tendons using multi-scale convolutional neural network with attention mechanism," Renewable Energy, Elsevier, vol. 199(C), pages 21-34.
    11. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
    12. Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    13. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
    14. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.
    15. Muhammad Shahid Mastoi & Hafiz Mudassir Munir & Shenxian Zhuang & Mannan Hassan & Muhammad Usman & Ahmad Alahmadi & Basem Alamri, 2022. "A Comprehensive Analysis of the Power Demand–Supply Situation, Electricity Usage Patterns, and the Recent Development of Renewable Energy in China," Sustainability, MDPI, vol. 14(6), pages 1-34, March.
    16. Rameshrao, Awagan Goyal & Koley, Ebha & Ghosh, Subhojit, 2022. "A LSTM-based approach for detection of high impedance faults in hybrid microgrid with immunity against weather intermittency and N-1 contingency," Renewable Energy, Elsevier, vol. 198(C), pages 75-90.
    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. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
    2. Zou, Runmin & Yang, Jiaxin & Wang, Yun & Liu, Fang & Essaaidi, Mohamed & Srinivasan, Dipti, 2021. "Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer," Applied Energy, Elsevier, vol. 304(C).
    3. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    4. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    5. Yanhui Qiao & Yongqian Liu & Yang Chen & Shuang Han & Luo Wang, 2022. "Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences," Energies, MDPI, vol. 15(5), pages 1-25, February.
    6. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
    7. Qian, Guo-Wei & Ishihara, Takeshi, 2022. "A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain," Energy, Elsevier, vol. 261(PA).
    8. Xiangqing Yin & Yi Liu & Li Yang & Wenchao Gao, 2022. "Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting," Energies, MDPI, vol. 15(17), pages 1-22, August.
    9. Pengfei Zhang & Zuoxia Xing & Shanshan Guo & Mingyang Chen & Qingqi Zhao, 2022. "A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging," Energies, MDPI, vol. 15(13), pages 1-15, July.
    10. Luo, Shihua & Hu, Weihao & Liu, Wen & Cao, Di & Du, Yuefang & Zhang, Zhenyuan & Chen, Zhe, 2022. "Impact analysis of COVID-19 pandemic on the future green power sector: A case study in the Netherlands," Renewable Energy, Elsevier, vol. 191(C), pages 261-277.
    11. Fan, Zhixin & Zhu, Caichao, 2019. "The optimization and the application for the wind turbine power-wind speed curve," Renewable Energy, Elsevier, vol. 140(C), pages 52-61.
    12. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    13. Li, B. & Zhou, D.L. & Wang, Y. & Shuai, Y. & Liu, Q.Z. & Cai, W.H., 2020. "The design of a small lab-scale wind turbine model with high performance similarity to its utility-scale prototype," Renewable Energy, Elsevier, vol. 149(C), pages 435-444.
    14. Lijuan Zhang & Tatyana Ponomarenko, 2023. "Directions for Sustainable Development of China’s Coal Industry in the Post-Epidemic Era," Sustainability, MDPI, vol. 15(8), pages 1-32, April.
    15. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    16. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    17. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    18. Ajith, Meenu & Martínez-Ramón, Manel, 2023. "Deep learning algorithms for very short term solar irradiance forecasting: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    19. Johansen, Katinka & Johra, Hicham, 2022. "A niche technique overlooked in the Danish district heating sector? Exploring socio-technical perspectives of short-term thermal energy storage for building energy flexibility," Energy, Elsevier, vol. 256(C).
    20. 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).

    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:215:y:2023:i:c:s0960148123008649. 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.