Optimisation of Wind Farms through Adaptive Wake Steering.
The problem
CURRENT OFFSHORE WIND FARM CONFIGURATION
Modern large–scale wind farms consist of multiple turbines clustered together. It is difficult to understand the optimal design of a wind farm as you must consider maximising the power output whilst minimising maintenance costs.
The solution
Using digital twin modelling and AI for Adaptive optimisation of wind farms
The wind farm design does not need to change but the wake steering of the turbines can be implemented to maximise energy production.
Wake steering is a wind farm control technique that works by intentionally misaligning some turbines with the wind to deflect their wakes away from downstream turbines.
OPTIMISING WAKE STEERING
Real time wake steering is a very complex solution due to highly dynamic offshore weather conditions and inaccessibility of the turbines. We are testing the potential of wake steering with a novel combination of an accurate digital twin of wind turbines and an AI controller to generate turbulent airflows around them.
The process learns how to optimise windfarm performance in the face of changing conditions through a trial-and-error process that adjusts based on feedback from the digital twin.
Our state-of-the-art technology can test real-life wake steering scenarios without the dangers and costs of testing on a real wind farm.
Wake steering improves wind farm efficiency by redirecting turbine wakes, allowing downstream turbines to receive stronger, less turbulent wind, which increases overall power output.
Wake steering enhances energy production and cost-effectiveness, contributing to more sustainable and profitable operations.
KEY FACTS
In 2024, wind power became the largest source of electricity generation in the UK. Wind power accounted for 30% of the UK’s electricity.
Surveys over a number of utility-scale wind farms have shown that wind turbine wake interactions are responsible for annual energy losses of up to 20%.
Studies show potential power output increases of 5–10% for optimized wind farms using wake steering to mitigate wake interactions.
RESEARCH TEAM
Prof. Sylvain Laizet
Wind Energy
Andrew Mole
Wind Energy