EPFL researchers have used a genetic learning algorithm to identify optimal pitch profiles for the blades of vertical-axis wind turbines, which are usually vulnerable to strong gusts of wind despite their high energy potential.
If you imagine an industrial wind turbine, you likely picture the windmill design, technically known as a horizontal-axis wind turbine (HAWT).
However, the first wind turbines, developed in the Middle East for grinding grain, were vertical-axis wind turbines (VAWT), meaning they spun perpendicular to the wind rather than parallel.
Engineering issues with vertical-axis wind turbines
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, making them easier for birds to avoid.
With these advantages, why are vertical-axis wind turbines largely absent from today’s wind energy market?
Sébastien Le Fouest, a researcher in the School of Engineering Unsteady Flow Diagnostics Lab, explains that 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.
“Our study represents, to the best of our knowledge, the first experimental application of a genetic learning algorithm to determine the best pitch for a VAWT blade,” Le Fouest said.
Turning issues into advantages
Le Fouest explained that while Europe’s installed wind energy capacity is growing by 19 gigawatts per year, this figure needs to be closer to 30 GW to meet the UN’s 2050 carbon emissions objectives.
He stated: “The barriers to achieving this are not financial, but social and legislative – there is very low public acceptance of wind turbines because of their size and noisiness.”
Despite their advantages, vertical-axis wind turbines suffer a severe drawback –they only function well with moderate, continuous airflow.
The vertical axis of rotation means that the blades constantly change orientation in relation to the wind. A strong gust increases the angle between airflow and the blade, forming a vortex called a dynamic stall. These vortices create transient structural loads that the blades cannot withstand.
To tackle this lack of resistance to gusts, the researchers mounted sensors onto an actuating blade shaft to measure the air forces acting on it.
They generated a series of ‘pitch profiles’ by pitching the blade back and forth at different angles, speeds, and amplitudes. Then, they used a computer to run a genetic algorithm that performed over 3,500 experimental iterations.
Like an evolutionary process, the algorithm selected the most efficient and robust pitch profiles and recombined their traits to generate new and improved ‘offspring’.
This approach allowed the researchers to identify two pitch profile series that contribute significantly to turbine efficiency and robustness, turning the biggest weakness of vertical-axis wind turbines into a strength.
Le Fouest explained: “Dynamic stall – the same phenomenon that destroys wind turbines – at a smaller scale can actually propel the blade forward.
“Here, we really use the dynamic stall to our advantage by redirecting the blade pitch forward to produce power.”
He concluded: “Most wind turbines angle the force generated by the blades upwards, which does not help the rotation.
“Changing that angle not only forms a smaller vortex, but it simultaneously pushes it away at precisely the right time, resulting in a second region of power production downwind.”
