Engineers at a UK university are developing a digital platform to help cut costs at wind farms.
WindTwin will use big data analytics and advanced visualisation and analysis to draw a real-time picture of the turbine’s condition, enabling live checks.
It will feed data from sound sensors on the turbines’ gearbox, generator and other mechanical parts into a 3D virtual model or “digital twin” that predicts what needs fixing and when.
Brunel University’s researchers believe that will help maintain and optimise real wind turbines, with upkeep costs expected to be reduced by up to 30%.
They add early breakdown detection will increase reliability by as much as 99.5% and reduce losses from downtime by 70%.
The platform will also allow operators to monitor and control entire wind farms digitally and remotely.
Dr Militiadis Kourmpetis at Brunel Innovation Centre said: “The savings could be vast – by 2025, running 5,500 offshore turbines could cost a yearly £2 billion – almost the same service bill as UK passenger planes.
“Our goal is to develop digital models or clones of a wind turbine which will combine mathematical models describing the physics of the turbine’s operation, with sensor data from actual parts during day-to-day running. These virtual models will allow wind farm operators to predict failure and plan maintenance, reducing maintenance costs and downtime.”