MS#10.6 Advanced Machine Learning for Wind Farm Optimisation and Maintenance
L. LANDBERG¹, T. GÖÇMEN², A. MEYER³
¹ DNV|² DTU|³ BFH and TU Delft
Emerging technologies and special sessions
This mini-symposium will explore and assess the potential of artificial intelligence applications in the operation and maintenance of wind farms. As wind energy plays an increasingly important role in energy systems, wind farm operation and maintenance can benefit from AI/ML techniques, for example, to improve control, maximize efficiency and reduce down times. Artificial intelligence and machine learning (AI/ML) hold the potential to provide real-time insights, decision support and automate complex processes. The mini-symposium aims to provide an overview of recent research progress in artificial intelligence applications in wind farm development, operation and maintenance.
Items to be covered:
- Advanced data analytics for wind farm condition monitoring
- AI/ML-driven predictive maintenance strategies and remaining useful life
- Reinforcement learning for adaptive control of wind turbines within a farm
- Physics-informed neural networks (PINNs) for improved wind field modelling/resource assessment
- Real-time performance analysis and optimisation using operational data
- Generative AI applications in wind turbine design and layout optimization
- ML-based fault detection and diagnosis of malfunctions, including hybrid models
- AI to address the lack of wind farm operation data sharing
- AI to enable data privacy and collaboration
- Digital twin technologies leveraging advanced ML techniques
- Emerging technologies and anything else of interest in the rapidly evolving field of ML applications for wind energy
We are looking for application of Machine Leaning systems that are at the state of the art level.
As part of the last session, we will have a panel debate with selected speakers from the line up, as well as invited speakers