Failure Avoidance for Wind Turbines through Fleetwide Control
Offshore wind farms are an indispensable driver toward renewable and non-polluting energy resources. However, placement in remote and challenging locations results in higher logistics costs and lower average wind speeds. Therefore, it is critical to increase the reliability of the turbines to reduce maintenance costs. We propose a multitiered data-driven approach to prevent failure based on experimental and field data.
Timothy Verstraeten graduated as MSc in Computer Science with a specialisation in AI at the VUB in 2015, and is currently a PhD student at the AI Lab Brussels. His PhD project focusses on the application of reinforcement learning in wind farm control, exploiting fleet-wide data exchange and loosely-coupled coordination among wind turbines to speed up the control learning process.