Topology-aware surrogate for future offshore wind farms using machine learning

As the role of offshore wind generation increases in modern power systems, the need for enhanced modelling techniques becomes critical. In problems involving iterative computations, simplified offshore wind farm models often disregard complex aerodynamic effects in order to reduce computational time. However, those effects have a significant impact on the produced electricity. To address this current modelling limitation, we develop a new topology-aware wind farm surrogate using supervised machine learning techniques. This surrogate model is trained to capture the complex relationship between free flow wind information and electrical output, irrespective of the wind farm layout. This allows bypassing the need to train a different model for each wind farm, while enabling the simulation of farms lacking historical data (such as those in construction phase). This is achieved by enriching the input space of the machine learning surrogate with novel geometric and physics-informed features, making it adaptable to any offshore wind farm configuration.