Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems

Authors:
Yu Ding;Eunshin Byon;Chiwoo Park;Jiong Tang;Yi Lu;Xin Wang
Affiliations:
Texas A&M University, College Station, TX 77843,;Texas A&M University, College Station, TX 77843,;Texas A&M University, College Station, TX 77843,;University of Connecticut, Storrs, CT 06269,;University of Connecticut, Storrs, CT 06269,;University of Connecticut, Storrs, CT 06269,
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Year:
2007

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Abstract

In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. The new methodology consists of robust data pre-processing modules, interrelated, multi-level models that describe different details of the system behaviors, and a dynamic strategy that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. This paper summarizes the latest progresses in the research.