Wind farms are becoming a key part of the energy landscape in many different ways. Both on-shore and off-shore, wind farms are providing more and more renewable energy, particularly for data centres that are handling petabytes of data every day.
The ownership and optimisation of these wind farms is also changing. For example, Apple has invested in a turbine in Denmark for wind power to a data centre there, and wind farms are regularly changing hands between owners.
Machine learning is already used for monitoring the performance of turbines and blades, providing preventive maintenance and reducing the number of times people have to climb up a tower to check on a turbine blade. But AI has distinct limits in the optimisation of wind farms, says Blair Heavey, CEO of energy consultancy Windesco in Boston, Mass.
“Outside of some unique spaces, AI hasn’t delivered,” said Heavey. “In some areas such as battery life there are standards where the data can be fed into the machine learning models and can provide almost human-like responses. What we found in the wind space is there isn’t anything that’s a standard and there’s no consistency across the data patterns that come in – different turbines, gearboxes, even different climate models, locations with a ton of different variables.”
Windesco is a consultancy in software analytics, helping wind power OEMs and farm operators develop more value from the assets. The human experience is essential for these highly complex systems, says Heavey.
“Our teams has been working on the algorithms for six years that we believe can tell the human expert what we should do but it requires an expert to oversee what the AI and the models are driving us towards,” he said.
“Because we don’t have all the standards, we could interpret that data correctly for one turbine and adjust the pitch and blades but that would damage a turbine