WindTwin will act like a pilots’ flight deck control panel for wind farm managers, giving them live condition checks on the working parts of each turbine.
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 which need fixing – and when. That lets companies scrap scheduled maintenance and replace or repair broken parts before they do damage.
Brunel University London is working on the £1.4 million 30-month WindTwin project with Agility3, ESI and TWI. Funded by the government’s Innovate UK, they plan to sell the digital twin platform worldwide and look at how other industries could use it.
Wind energy is the cheapest form of new power generation and has the potential to provide 30% of Europe’s power by 2030, according to WindEurope.
“The data this software generates has huge potential benefits for the wind turbine industry,” said Dr. Miltiadis Kourmpetis, at Brunel Innovation Centre.
“The savings could be vast – by 2025, running 5,500 offshore turbines could cost a yearly £2 billion in inspection and maintenance – almost the same service bill as UK passenger planes.
“Our goal is to develop digital models or clones of a wind turbine which 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.”
The digital twin platform will use big data analytics and advanced visualisation and analysis to draw a real-time picture of the turbine’s condition. This will help maintain and optimise real wind turbines, cutting upkeep costs by up to 30%, researchers calculate.
Early breakdown detection will up reliability by as much as 99.5% and reduce losses from downtime by 70%. It also lets workers monitor and control entire wind farms digitally and remotely.
Digital twin technology is already changing manufacturing and forecasters predict billions of things will represented by a digital twin with aerospace, oil and gas and transport at the forefront.
17 Oct 2017