In a significant advancement for the wind energy sector, researchers have unveiled a cutting-edge control strategy designed to optimize the mechanical loads on wind turbines. This innovative approach, spearheaded by Deyi Fu from the National Key Laboratory of Renewable Energy Grid‐Integration at the China Electric Power Research Institute, employs a sophisticated wind speed estimator utilizing a time series Broad Learning System Method (BLSM).
Traditionally, wind turbines have relied on anemometers mounted on their nacelles to gauge incoming wind characteristics. This passive approach often leaves turbines vulnerable to unpredictable wind conditions, potentially leading to excessive mechanical stress and reduced lifespan. Fu’s research aims to change that paradigm by proactively estimating wind speeds, allowing turbines to adjust their operations in real-time and significantly enhance their performance.
“The implementation of our optimization control strategy can substantially mitigate both ultimate and fatigue loads on wind turbines,” Fu stated. His findings reveal a notable reduction in fatigue loads, particularly in the tower base tilt and roll bending moments, with reductions of approximately 6.2% and 4.3%, respectively. These improvements not only enhance the reliability of wind turbines but also promise to extend their operational lifespan, a critical factor for energy companies operating in a competitive market.
The research, published in the journal ‘IET Control Theory & Applications’—translated as ‘IET Control Theory and Applications’—highlights the potential for commercial impact in the renewable energy sector. By reducing mechanical load stresses, energy providers can expect lower maintenance costs and improved energy output, which could lead to greater profitability in an industry that is increasingly focused on sustainability and efficiency.
Fu’s team conducted comprehensive simulations using OpenFAST, a well-regarded tool for wind turbine analysis, to compare mechanical load characteristics before and after implementing the new control strategy. The results not only validate the effectiveness of the BLSM approach but also set a new benchmark for future research and development in wind turbine technology.
As the world continues to pivot towards renewable energy sources, innovations like Fu’s optimization control strategy could play a pivotal role in shaping the future of wind power. By enhancing the performance and durability of wind turbines, this research not only addresses immediate operational challenges but also contributes to a more sustainable energy landscape.
For more information about the research team and their work, visit National Key Laboratory of Renewable Energy Grid‐Integration China Electric Power Research Institute.
