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

Changhyun Kim, Minh-Chau Dinh, Hae-Jin Sung, Kyong-Hwan Kim, Jeong-Ho Choi, Lukas Graber, In-Keun Yu and Minwon Park ()
Additional contact information
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
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

Energies, 2022, vol. 15, issue 17, 1-16

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.

Keywords: digital twin; hybrid analysis and modeling; reduce order model; offshore wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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