The collective efforts in aerospace, civil, electrical, and mechanical engineering areas have led to remarkable progresses in wind energy. Larger turbines are designed and installed, and wind farms are nowadays built at locations where wind is even more intermittent and maintenance equipment is less accessible. This adds new challenges to ensuring operational reliability. To cope with these challenges, along with the rapid advancement in microelectronics, modern wind farms are equipped with a large number and variety of sensors, including, at the turbine level, anemometers, tachometers, accelerometers, thermometers, strain sensors, and power meters, and at the farm level, anemometers, vanes, sonars, thermometers, humidity meters, pressure meters, among others. It is worth noting that all these data are currently analyzed/utilized only in their respective domains. The big data challenges in this project include how to best use spatio-temporal data for wind forecast, how to use data of different nature (wind, power, load etc.) and data of different sources (physical data versus computer simulation data) for power production assessment in a computationally efficient manner, and finally how to integrate these three sets of solutions into a reliable and efficient computational platform. The proposed research and education activities will make a paradigm shift in the wind industry by demonstrating how dramatically data science innovations can benefit the industry. The PIs will disseminate the research findings through classroom teaching, journal/conference publications, industry workshops, and data/software sharing. The summer internship opportunities and undergraduate research help train the next generation workforce to be better versed with data science methodologies.

The critical barrier to cost effective wind power and its general adoption is partly rooted in wind stochasticity, severely complicating wind power production optimization and cost reduction. The long-term viability of wind energy hinges upon a good understanding of its production reliability, which is affected in turn by the predictability of wind and power productivity of wind turbines. Furthermore, the productivity of a wind turbine comprises two aspects: its ability of converting wind into power during its operation and the availability of wind turbines. Three inter-related research efforts will enhance wind energy reliability and productivity): (1) spatio-temporal analysis (for wind forecast) (2) conditional density estimation (for wind-to-power conversion assessment); and (3) importance sampling (for turbine reliability assessment and improvement). Significant data resourced provided by industry partners in the research, coupled with models and computational resources, will enable better prediction of wind profiles and utilization. In addition, the team will develop dedicated reconfigurable field programmable gate array (FPGA) processors that will be 50 to 500 times faster than general-purpose CPUs for both on-site and central control processing and have small form-factor, low cost and energy efficient to enable agile development under severe outdoor conditions at wind farms.

Agency
National Science Foundation (NSF)
Institute
Division of Information and Intelligent Systems (IIS)
Type
Standard Grant (Standard)
Application #
1741173
Program Officer
Sylvia J. Spengler
Project Start
Project End
Budget Start
2017-10-01
Budget End
2020-09-30
Support Year
Fiscal Year
2017
Total Cost
$749,799
Indirect Cost
Name
Texas A&M Engineering Experiment Station
Department
Type
DUNS #
City
College Station
State
TX
Country
United States
Zip Code
77845