Predictive digital twin for offshore wind farms

• Published in: Energy Informatics
• Main Authors: Amirashkan Haghshenas, Agus Hasan, Ottar Osen, Egil Tennfjord Mikalsen
• Format: Article
• Language: English
• Published: SpringerOpen 2023-01-01
• Subjects: Digital twin; Wind energy; Predictive maintenance
• Online Access: https://doi.org/10.1186/s42162-023-00257-4

Abstract As wind turbines continue to grow in size, they are increasingly being deployed offshore. This causes operation and maintenance of wind turbines becoming more challenging. Digitalization is a key enabling technology to manage wind farms in hostile environments and potentially increasing safety and reducing operational and maintenance costs. Digital infrastructure based on Industry 4.0 concept, such as digital twin, enables data collection, visualization, and analysis of wind power analytic at either individual turbine or wind farm level. In this paper, the concept of predictive digital twin for wind farm applications is introduced and demonstrated. To this end, a digital twin platform based on Unity3D for visualization and OPC Unified Architecture (OPC-UA) for data communication is developed. The platform is completed with the Prophet prediction algorithm to detect potential failure of wind turbine components in the near future and presented in augmented reality to enhance user experience. The presentation is intuitive and easy to use. The limitations of the platform include a lack of support for specific features like electronic signature, enhanced failover, and historical data sources. Simulation results based on the Hywind Tampen floating wind farm configuration show our proposed platform has promising potentials for offshore wind farm applications.