MS#06.1 Data Synergies in Wind: Process Modelling Beyond Power Generation
T. GÖÇMEN¹, I. EGUINOA ERDOZAIN²
¹ DTU Wind and Energy Systems|² CENER
Reliability, monitoring and sensing, O&M
The session is open to all interested in the following topics:
- Data Fusion Techniques for Comprehensive Wind Farm Performance Assessment
Exploring methods to integrate diverse data sources (SCADA, environmental, operational – both physical and computational data) for a holistic understanding of wind farm performance beyond power output. - Data-Driven Risk Analysis and Failure Mode Prognosis
Leveraging advanced analytics to evaluate the effects of different wind farm operation on wind turbine component failure rates, comparing them to baseline scenarios, and providing critical inputs for lifecycle assessments. - Virtual Sensor Development and augmentation of physical data
Exploring the potential of virtual sensors to reduce hardware costs, improve reliability, and enable more sophisticated monitoring and operation strategies in wind energy systems. - Data-Enabled Social and Environmental Impact Modelling
Addressing how various data types can be leveraged to model and mitigate the social and environmental impacts of wind farm operations. - Advanced Data Quality Control and Assurance for Wind Farm Operations
Examining techniques for ensuring data integrity and reliability in wind farm monitoring and analysis, including real-time and historical data processing methods.
This mini-symposium is organised as part of the TWAIN project (Integrated, Value-based, and Multi-objective Wind Farm Control powered by Artificial Intelligence). TWAIN is funded by the European Union's Horizon Europe research and innovation programme under grant agreement No. 101122194. For more information about TWAIN, please visit: https://twainproject.eu