Computational Modeling

Innovative Wind Turbine Control Earns Lucy Pao Prestigious IEEE Award

Daniel Morton — Thu, 19 Dec 2024 15:03:00 +0000

Innovative Wind Turbine Control Earns Lucy Pao Prestigious IEEE Award Daniel Morton Thu, 12/19/2024 - 08:03

Daniel Morton

Renewable and Sustainable Energy Institute (RASEI) Fellow Lucy Pao, from the Department of Electrical, Computer, and Energy Engineering at ��«��Ƶ, is travelling to Milan, Italy, this December to receive the IEEE Transactions on Control Systems Technology Outstanding Paper Award.

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IEEE Conference on Decision and Control

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ForWind Center

Collaboration Highlight

Each year a panel reviews papers nominated from the past two years and selects an article of high significance. The paper, published in 2022, describes the first experimental implementation of Model Predictive Control of the pitch angles of the blades in wind turbines. The Award Ceremony will be held on December 18, 2024, as part of the 2024 IEEE Conference on Decision and Control, held at the Milan Convention Centre in Italy.

Model Predictive Control (MPC) is an established control technique that is popular in the general control systems community. One of the primary demonstrated applications for MPC is in process chemistry for the large-scale manufacture of pharmaceuticals and commodity chemicals. This paper describes, for the first time, the application of this technique in the blade pitch control of wind turbines. Control systems in wind turbines are essential for optimizing their performance, ensuring safety, and maximizing energy output. By monitoring key parameters such as blade pitch, rotor speed, yaw angle, and blade root bending moment, the control system can adapt to changing wind conditions, enhancing the efficiency of power generation and reducing mechanical stress on components, helping to extend the turbine’s lifespan.

“MPC is a very nice approach, but it is computationally complex,” explained Lucy. “There is an optimization that needs to be done at every time step, so getting it into a form where we could implement it, was a challenge”.

While MPC is a computationally intensive approach, the benefits for wind turbines are significant. Many wind turbine control systems are based on a feedback-only system, where the adjustments are based on feedback of what is happening now. MPC enables preview information to be included, so, for example, you can incorporate knowledge of a gust coming through and the controls can prepare the turbine ahead of time, which can reduce structural loading on that turbine. It changes the control system from simply being responsive to stimuli, to instead being proactive to knowledge of the conditions, essentially letting the wind turbine “see into the future”.

The MPC approach could have significant impacts on how wind turbines are controlled, not only improving their efficiency, but also reducing structural stress on the turbines and extending their lifetimes.

“We are really very excited about this award,” said Pao. “This has been a wonderful collaboration with our colleagues at the ForWind Center for Wind Energy Research in Oldenburg Germany. We started in 2016, with an experimental campaign in 2019 that led to this paper”. Misha Sinner, the lead author of the study, was a graduate student working with Lucy Pao and is now a Research Engineer at the National Renewable Energy Laboratory (NREL). Misha travelled to Germany to lead this experimental campaign, and worked together with the team in Germany, led by Martin Kühn, to apply MPC to the turbines. “This was a complex experiment to run, and it wouldn’t have been possible without the team of experts and infrastructure at ForWind,” said Misha. “The collaboration has been a really symbiotic relationship; I really value Martin’s input, support, and feedback on the work,” said Lucy.

The collaboration was sparked over a meeting in Stuttgart in 2015. “Martin and I were both external evaluators on a PhD defense for a student at the University of Stuttgart. We had lots of opportunities to chat, and when I mentioned that I was coming up on a sabbatical, he suggested a fellowship that I could apply for that would enable me to work with them,” explained Lucy. This was the start of the long and fruitful collaboration among the team.

There is hope that the recognition that the award brings will accelerate adoption of advanced control systems by the wind energy industry. “MPC can take into account constraints on the wind turbine system, and it is flexible enough to incorporate preview wind information,” said Lucy. This could provide significant improvements in the efficiency and lifetime of future wind turbines. “It is a really nice control framework, and while it has been looked at in the simulation context, it had not been examined experimentally for blade pitch control, to our knowledge,” said Lucy. “We have heard that Industry is looking at MPC, but we don’t know whether it has made it into their actual controllers. The wind turbine industry can be quite secretive about what they are doing”.

The MPC approach could have significant impact on future, and existing wind turbines, by giving the control system the chance to effectively “see into the future” by a couple to tens of seconds so the turbine can prepare itself. “We are very excited and really touched by this award,” emphasized Lucy, with the hope that this will catch the attention of those in the wind turbine industry.

December 2024

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Daniel Morton — Wed, 20 Nov 2024 18:50:57 +0000

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Anonymous — Thu, 08 Aug 2024 06:00:00 +0000

R&D 100 Winners for 2024 are Announced Anonymous (not verified) Thu, 08/08/2024 - 00:00

The R&D 100 Awards are a renowned worldwide science and innovation competition celebrated their 62nd year in 2024. Entries were received from 16 different countries, and RASEI Fellow Steven Spurgeon led one of the winning teams.

The Autonomous Electron Microscope (AutoEM) received the 2024 R&D 100 award! AutoEM is the result of nearly 7 years of hard work by a dedicated team of scientists, including Steven R. Spurgeon (NREL, formerly PNNL), Matthew Olszta, Sarah Akers, Kevin Fiedler, Derek Hopkins, Jacob Haag, Kayla Yano, Christina Doty, and Marjolein Oostrom (PNNL).

AutoEM embeds human-like reasoning into the control of electron microscopes, which are a cornerstone of the study of materials and chemical systems. Using AutoEM researchers can conduct reproducible, high-speed experimentation nearly 1000x faster than before. We’re excited for the possibilities that AutoEM will unlock, as well as ongoing commercialization efforts with JEOL-IDES, one of the world’s largest analytical instrumentation manufacturers.

The R&D 100 Awards, often referred to as the "Oscars of Innovation," are prestigious annual awards recognizing the 100 most technologically significant products, processes, materials, or software introduced to the market in the previous year. The awards, established in 1963, are open to submissions from around the world and are judged by a panel of experts based on technical significance, uniqueness, and usefulness. Winning an R&D 100 Award is a mark of excellence for innovators and their organizations, signifying the development of groundbreaking and commercially promising technologies.

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