Yu Ding, Ph.D.
Mike and Sugar Barnes Professor of Industrial & Systems Engineering
Texas A&M University
Abstract: Data Science for Wind Energy: Wind Turbine Performance and Reliability Assessment
Wind energy is the front-runner among the renewable energy sources. U.S. Department of Energy envisions that wind will generate 20% of the nation’s electricity by 2030 and 35% by 2050. The ever-changing wind exerts a non-stationary and non-steady load on wind turbine drive train, causing wind turbines to deteriorate faster than other turbine machineries. Other harsh environmental conditions such as icing and lighting add to low reliability of wind turbines, which in turn drives up the cost of operations and maintenance. To this day and in the foreseeable future, the market competiveness of wind energy is still an issue, especially when government subsidy disappears. In this talk, we will discuss the performance and reliability issue in wind energy and the data science relevance. Much of the work is in the speaker’s book, Data Science for Wind Energy, published in 2019.
Department of Industrial & Systems Engineering