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Ensuring the Future Safety of Windfarms

Ensuring the Future Safety of Windfarms

The Challenge

Wind turbines make an important contribution to reducing greenhouse emissions. In the UK we have over 30GW of energy generation using both onshore and offshore turbines.

An unwanted side-effect of these large rotating blades is to create interference in radar systems; the motion of the blades causes them to look similar to legitimate targets (e.g. aircraft) to the radar. This is a particular problem in air traffic control radar where early detection of small aircraft at distance is critical. The ‘clutter’ generated by wind turbines can reduce the ability to do this by creating false tracks that radar operators need to assess, increasing their cognitive burden and diverting attention away from real targets.

The Approach​

Differentiation between the radar track generated by a real aircraft and a wind turbine has proved difficult for conventional classification and tracking algorithms. Through a combination of modelling, simulation and scaled experiments, the aim was to demonstrate that the latest Deep Learning methods could solve this problem.

The Outcome

The application of machine learning to this problem allows the system to ‘learn’ to recognise the characteristics of clutter signals from windfarms. This creates the opportunity to reject them or to tag them for the operator.

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