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Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin

Author

Listed:
  • Changhyun Kim

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

  • Minh-Chau Dinh

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Hae-Jin Sung

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Kyong-Hwan Kim

    (Korea Research Institute of Ships & Ocean Engineering, Daejeon 34103, Korea)

  • Jeong-Ho Choi

    (Korea Electric Power Corp, Naju 58322, Korea
    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 20332, USA)

  • Lukas Graber

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 20332, USA)

  • In-Keun Yu

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Minwon Park

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

Abstract

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.

Suggested Citation

  • Changhyun Kim & Minh-Chau Dinh & Hae-Jin Sung & Kyong-Hwan Kim & Jeong-Ho Choi & Lukas Graber & In-Keun Yu & Minwon Park, 2022. "Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin," Energies, MDPI, vol. 15(17), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6329-:d:902002
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    References listed on IDEAS

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    5. Jinje Park & Changhyun Kim & Minh-Chau Dinh & Minwon Park, 2022. "Design of a Condition Monitoring System for Wind Turbines," Energies, MDPI, vol. 15(2), pages 1-16, January.
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    Cited by:

    1. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    2. Lv, Zhihan & Cheng, Chen & Lv, Haibin, 2023. "Digital twins for secure thermal energy storage in building," Applied Energy, Elsevier, vol. 338(C).
    3. Konstantinos Prantikos & Lefteri H. Tsoukalas & Alexander Heifetz, 2022. "Physics-Informed Neural Network Solution of Point Kinetics Equations for a Nuclear Reactor Digital Twin," Energies, MDPI, vol. 15(20), pages 1-22, October.
    4. Sinawo Nomandela & Mkhululi E. S. Mnguni & Atanda K. Raji, 2023. "Modeling and Simulation of a Large-Scale Wind Power Plant Considering Grid Code Requirements," Energies, MDPI, vol. 16(6), pages 1-24, March.
    5. Xiaotong Dong & Jing Huang & Ningzhao Luo & Wenshan Hu & Zhongcheng Lei, 2023. "Design and Implementation of Digital Twin Diesel Generator Systems," Energies, MDPI, vol. 16(18), pages 1-16, September.

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