Extreme weather sufficiently affect the renewable energy converting efficiency, which may cause sudden and severe energy deficits, especially for small and isolated power systems, such as frozen wind turbines in Texas that experienced power outages in 2021. A better understanding of the impact of extreme weather (i.e., icing, and low temperature) on wind turbine power generation would assist the power system operator in forming effective decisions and increasing system integrity.
First, lab-scale experiments were conducted to shed light on the fundamental icing physics and explore effective ice mitigation strategies. Specifically, by leveraging the Icing Research Tunnel of Iowa State University, a comprehensive experimental study was conducted to quantify the transient behavior of the surface water transport process over ice accreting surfaces of typical wind turbine blade models by using a novel Digital Image Projection (DIP) technique. The aerodynamic performance degradation of the turbine blade models was characterized during the ice accreting process by using a high-sensitive multi-axis force/moment system and a digital Particle Image Velocimetry (PIV) system. A novel hybrid anti-icing strategy that combines minimized electro-heating near the turbine blade leading edge and bio-inspired icephobic coatings to cover the blade surface was proposed. Second, field campaigns were conducted in wind farms to fill the knowledge gaps between lab-scale investigations and practical applications. The ice-induced performance degradation of multi-megawatt wind turbines was characterized by correlating the acquired images of ice accretion over the rotating wind turbine blades with an unmanned aerial vehicle (UAV) with the turbine operational data recorded by wind turbine supervisory control and data acquisition (SCADA) systems. Third, based on the findings derived from lab-scale experiments and field campaigns, fast and robust statistical models were developed to forecast the icing losses associated with precipitation icing, frost contamination, and low temperature. The proposed model can be easily integrated into the existing wind farms and power grid operations to achieve regional and national forecasts.
About the Speaker
Linyue Gao joined the Department of Mechanical Engineering at California State University, Sacramento (Sac State) as an Assistant Professor in August 2021. She received her B.S and M.S in Renewable Energy from North China Electric Power University in 2013 and 2016. She received her Ph.D. degree in Aerospace Engineering from Iowa State University in 2019. She was awarded the Brown Graduate Fellowship and President’s Fellowship here. Her dissertation work under the supervision of Prof. Hui Hu is going to be published in a book, titled “Wind Turbine Icing Physics and Anti-/De-icing Technology”, this June by Elsevier. Before joining Sac State, she worked as a Postdoctoral Associate in the St. Anthony Falls Laboratory at the University of Minnesota, Twin Cities, for 2 years. She was awarded Renewable Energy Commercialization Fellowship. Her primary research focuses on developing advanced flow diagnostic techniques and exploring their applications in renewable energy, material science, and health systems.
Host: Professor Leo Chamorro