Machine learning enables viability of vertical-axis wind turbines
EPFL researchers have used a genetic learning algorithm to identify optimal pitch profiles for the blades of vertical-axis wind turbines, which despite their high energy potential, have until now been vulnerable to strong gusts of wind.
If you imagine an industrial wind turbine, you likely picture the windmill design, technically known as a horizontal-axis wind turbine (HAWT). But the very first wind turbines, which were developed in the Middle East around the 8th century for grinding grain, were vertical-axis wind turbines (VAWT), meaning they spun perpendicular to the wind, rather than parallel.
Due to their slower rotation speed, VAWTs are less noisy than HAWTs and achieve greater wind energy density, meaning they need less space for the same output both on- and off-shore. The blades are also more wildlife-friendly: because they rotate laterally, rather than slicing down from above, they are easier for birds to avoid.
With these advantages, why are VAWTs largely absent from today’s wind energy market? As Sébastien Le Fouest, a researcher in the School of Engineering Unsteady Flow Diagnostics Lab (UNFOLD) explains, it comes down to an engineering problem – air flow control – that he believes can be solved with a combination of sensor technology and machine learning. In a paper recently published in Nature Communications, Le Fouest and UNFOLD head Karen Mulleners describe two optimal pitch profiles for VAWT blades, which achieve a 200% increase in turbine efficiency and a 77% reduction in structure-threatening vibrations.
