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

Author:

Kim Changhyun,Dinh Minh-Chau,Sung Hae-Jin,Kim Kyong-HwanORCID,Choi Jeong-Ho,Graber Lukas,Yu In-Keun,Park Minwon

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.

Funder

Changwon National University

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Reference29 articles.

1. Query Tool, Renewable Electricity Capacity and Generation Statistics https://www.irena.org/Statistics/View-Data-by-Topic/Capacity-and-Generation/Query-Tool

2. Distribution of Wind Power Generation Dependently of Meteorological Factors;Rubanenko;Proceedings of the 2020 IEEE KhPI Week on Advanced Technology (KhPIWeek),2020

3. Fault Tree Analysis of floating offshore wind turbines

4. Survey of failures in wind power systems with focus on Swedish wind power plants during 1997–2005;Ribrant;IEEE Trans. Energy Convers.,2007

5. Assessment of the Power Quality in Electric Networks with Wind Power Plants;Gundebommu;Proceedings of the 2020 IEEE 7th International Conference on Energy Smart Systems (ESS),2020

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