Ahmed 'Aziz' Ezzat, Ph.D. student and research assistant in the Department of Industrial and Systems Engineering at Texas A&M University, was selected to receive the Best Presentation Award from the Texas A&M Energy Conference for his presentation “Space-time modeling of asymmetric local wind fields.”
“The ultimate goal of this project is to bridge the gap between statistical modeling and engineering practice in wind energy systems by the characterization and modeling of complex real-world physical phenomena in wind dynamics,” Ezzat said.
Ezzat’s presentation addressed the exploratory analysis of wind dynamics in a space-time domain characterized by a dense grid of locations with close spatial and temporal proximity. This work can be applied to a set of wind turbine measurements on a wind farm.
“The interaction between the spatial and temporal components in wind dynamics is commonly overlooked by using separable, fully-symmetric statistical models,” Ezzat said. “Our analysis suggests that wind dynamics in wind farms are asymmetric in nature.”
While still in the early stages, this research will provide a deeper understanding of wind dynamics and enable better forecasts and robust control strategies in wind energy applications.
“The next step of this project is the development of space-time statistical models for the inference and prediction of wind speed profile and power production in wind farms,” Ezzat explained.
The conference, organized by the Texas A&M Energy Research Society in partnership with the Texas A&M Energy Club, covered all facets of energy during the span of three days.
“Being selected from more than 100 presentations, this award is a step forward and a confirmation that, as a research team, we are headed in the right direction of genuine and novel research in the area of statistical modeling of wind power systems,” Ezzat said.
Ezzat studies the space-time statistical modeling in engineering systems under Dr. Yu Ding, the Mike and Sugar Barnes Professor in the Department of Industrial and Systems Engineering, with emphasis in the forecast, understanding and control of wind energy applications.