at another locations. We always believe in AI and physics based models, but before we implement these models we oversee the unique data of that particular turbine in that particular farm,” he said.
A digital twin is one approach that is being considered by some operators to handle the complexity of a wind farm, but it has limitations, says Heavey. Siemens Energy is a major OEM of wind turbines, and digital twin technology is a strong technology capability for Siemens.
“It’s difficult to put these things together,” he said. “Some of the additional modelling you will see us bring out to the market next year is wake steering, that’s something that we are modelling and have proof of concept algorithms put together. The added complexity of those models is not only the blade turbine and gearbox, but in a single area you have turbines communicating with each other and that can have downstream turbines do a bunch of different things.”
This can extend the life of the turbines, avoiding running them when the wind is too strong or out of range. “We already use fluid dynamics, physics and a broader ecosystem of academics to challenge, test and implement algorothms on smaller wind farms to make sure we do no harm,” said Heavy. “Our customers are increasingly comfortable with the AI and algorithms that are running and sharing them back with us to improve the algorithms. The trust factor that customers have is applying the human oversight to ensure that the system works and delivers what is anticipated.”
This helps to build the models that help to improve the performance and efficiency of wind farms.
“Customers are sharing more data with us long term, constantly working to improve that efficiency as long as it is anonymised to help the overall industry. I think we are still educating people on this,” he said.
The problem is there