How Machine Learning and DAQ are Driving Energy Sustainability Forward​

When you think of wind energy, the image of towering turbines with rotating blades likely comes to mind. These horizontal axis wind turbines (HAWTs), known for their iconic design, generate the majority of the world’s wind energy. Their efficiency is driven by advanced technologies, including blade pitch control—a system that adjusts blade angles to optimize energy output.

Companies like Vestas have perfected blade pitch control for HAWTs, enabling real-time adjustments with minimal disruption to operations. However, despite their dominance, HAWTs require significant investment in design, installation, and maintenance, creating a need for alternative solutions.

Enter vertical axis wind turbines (VAWTs). These turbines are gaining attention for their lower cost, reduced noise, and suitability for urban environments. Yet challenges such as dynamic stall—a phenomenon where gusts cause structural stresses—and load fluctuations have limited their adoption. Recent advancements in machine learning, paired with data acquisition (DAQ) technology, are poised to change that.

Revolutionizing Blade Pitch Control with Machine Learning
For VAWTs, blade pitch control is pivotal. Adjusting blade angles in response to wind conditions helps mitigate dynamic stall and reduce structural stress. Researchers at the École Polytechnique Fédérale de Lausanne recently applied machine learning to this problem, using genetic algorithms to identify optimal pitch profiles. The results were remarkable: a 200 percent increase in efficiency and a 77 percent reduction in harmful vibrations.

Machine learning enables turbines to continuously analyze data from embedded sensors, adapting blade pitch in real time to maximize efficiency and minimize wear. This innovation not only enhances performance but also extends the lifespan of turbines, paving the way for broader adoption of VAWTs in energy generation—especially in space-constrained urban settings.

The Role of DAQ in Intelligent Control Systems
Machine learning depends on high-quality data. In the Lausanne study, researchers relied on DAQ systems to capture key performance metrics, including rotational speed, structural strain, and airflow dynamics. Advanced technologies like particle image velocimetry and strain gauges provided the data needed to train the machine learning algorithm and validate its performance.

For large-scale VAWT deployments, the complexity of DAQ systems increases. These systems must synchronize and process vast amounts of data across distributed networks, enabling real-time adjustments to blade pitch. Precision, timing, and scalability are critical to ensuring the success of such intelligent systems.

Accelerating the Transition to Sustainable Energy
While we can’t control the wind, we can harness its power more effectively with the right tools. From pioneering HAWT solutions to advancing VAWT technology, NI’s DAQ and control systems play a key role in enabling innovation.

Here’s why NI stands out for these applications:

By addressing the challenges of dynamic stall and structural stress, machine learning and DAQ are enhancing the viability of VAWTs, contributing to a more sustainable energy future. These advancements support global carbon reduction goals and underscore the potential of innovative technologies in driving energy transformation.