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Lucy Pao (left) accepts the IEEE Transactions on Control Systems Technology Outstanding Paper Award in Milan, Italy.

Photo Credit: IEEE Control Systems Society

In order for wind turbines to function effectively across wide ranges of wind conditions, you’ll need what’s known as blade pitch control.��

Lucy Pao, the Palmer Endowed Chair Professor in the Department of Electrical, Computer and Energy Engineering at ŷ���ڱ���Ƶ Boulder, was honored by the�� for advancing research in wind turbine control systems.��

Her IEEE Transactions on Control Systems Technology Outstanding Paper Award recognized the work with her former PhD student Michael N. Sinner, now a researcher at the National Renewable Energy Laboratory (NREL) and collaborators from ForWind – Center for Wind Energy Research in Germany.

Advancing Wind Energy Through Control Systems

In the award-winning paper, Pao’s team explored how advanced control methods, specifically a model predictive control (MPC) framework, can optimize blade pitch control on wind turbines.��

Blade pitch control—the adjustment of a wind turbine’s blade angle—is crucial for regulating rotor speed and mitigating structural loads, particularly during gusty or turbulent wind conditions.

The study demonstrated how incorporating wind information, measured in this case with anemometers in a wind tunnel, can significantly improve the performance of wind turbines. By anticipating wind conditions before they reach the turbine, the system optimizes blade pitch adjustments in real-time, reducing wear and tear on turbine components and enhancing energy efficiency.

“With just a little bit of preview information, we were able to start pitching the blades ahead of a gust of wind,” Pao explained. “This reduces structural loads and regulates generator speed more effectively than feedback-only control systems.”

Bridging the Gap Between Theory and Practice

While MPC is a well-known method in control systems, its application to wind turbines represents a leap forward in the field. Traditionally used in industries with slower dynamic systems, such as chemical processing, MPC has not been widely adopted in fast-moving systems due to its computational complexities.��

Dr. Pao’s team addressed this challenge by successfully implementing MPC on a fully instrumented, scaled wind turbine in a state-of-the-art wind tunnel at the University of Oldenburg’s ForWind Center in Germany.

“Our study proves that model predictive control can be implemented in real-time, even in dynamic systems like wind turbines,” said Dr. Pao. “Our findings pave the way for future adoption of this technology in commercial wind turbines, potentially transforming the wind energy sector.”

Collaboration Across Continents

The research is the culmination of a long-standing collaboration with the ForWind Center, initiated during Pao’s sabbatical in Germany in 2016.

“This collaboration began almost a decade ago with an exchange student and has since grown into a strong partnership,” Pao said. “We’ve exchanged students and postdocs, conducted joint experiments and built a shared vision for advancing wind energy.”

Michael Sinner’s involvement in the project is a testament to this collaboration. During his PhD, Sinner worked extensively with the ForWind Center’s advanced wind tunnel facility, which enabled precise and repeated experiments.

“Wind tunnel testing allows us to replicate conditions and isolate variables in ways that are challenging in open-field testing,” she said. “This control and consistency were critical for validating our findings.”

Looking to the Future

Pao’s collaborators have already begun follow-up studies, exploring the sensitivity of the control system to varying wind information and optimization horizon lengths. Preliminary results suggest the control approach is robust even when the predicted timing of the incoming wind, like a gust, is slightly off, which is encouraging for future field applications.

“We’re excited to see how this technology could be tested on full-scale turbines in the field,” Dr. Pao said. “The wind energy industry is already expressing interest, and we believe these advancements could have a significant impact.”

Beyond the technical achievements, the collaboration with ForWind continues to thrive. The partnership has facilitated ongoing exchanges, such as the current work of Juan Boullosa, a master’s student from Oldenburg University, who is contributing to wind field forecasting and optimization algorithms in Pao’s lab at ŷ���ڱ���Ƶ Boulder through the��Europe-ŷ���ڱ���Ƶ Program.

The intersection of advanced control systems and renewable energy continues to offer groundbreaking opportunities for innovation and global collaboration. Reflecting on the award, Pao expressed gratitude for the recognition.��

“It’s a celebration of collaborative effort and the potential for meaningful impact, so it’s a tremendous honor.”