Page 12 - ME News Fall 2021

UMN ME PREVENTING ICE-INDUCED ENERGY LOSS
ON WIND FARMS
Turbine icing in cold climates can cause significant energy loss, leading to a reduction in reliability of wind energy. To combat this, Lauren Swenson, an ME class of 2020 undergraduate student, conducted research to develop a new, fast framework for wind farm icing loss forecasts. Swenson’s work was part of her honors thesis, and was supervised by Professor Lian Shen, together
with Dr. Linyue Gao, a postdoctoral
associate supervised by Associate Professor Jiarong Hong. The contributions of this study are as follows:
• It proposes a fast and efficient framework for wind farm icing loss forecasting.
• It fills a gap between meteorological icing, wind turbine icing, and corresponding energy losses.
• It enables the icing loss estimation for wind farms.
For decades, the wind farm power forecast has lacked information on icing-induced energy loss, which may add severe risks to the power system under extreme icing events, such as the February 2021 blackouts in Texas. The novelty of this work lies in the fact that it provides a fast and efficient framework for the forecasts of icing- induced energy losses for wind farms in large areas. The proposed method has been validated using the Morris wind turbine and further implemented in 30 large-scale wind farms in the Midwest.
This contribution is important because it offers a fast, computationally efficient method for the forecast of the icing- induced energy losses for wind farms in large areas. Such forecasts increase the integrity of the power grid under wind extremes. In addition, the method also can be used
for planning new wind farms.
To learn more about the research being done at UMN ME, visit our website:
cse.umn.edu/me
12 ME News Fall 2021
Credit: Saint Anthony Falls Lab