DDDAS - SMRP: A Framework For the Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

CMS-0540132, PI: Yu Ding, Texas A&M University CMS-0540278, PI: Jiong Tang, University of Connecticut Abstract This collaborative research (0540132, PI: Yu Ding, Texas A&M University; and 0540278, PI: Jiong Tang, University of Connecticut) will provide a dynamic data-driven framework for wind turbine diagnosis. 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. This framework consists of two robust data pre-processing modules for highlighting fault features and removing sensor anomaly, three interrelated, multi-level models that describe different details of the system behaviors, and one dynamic strategy for the robust local interrogation that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. It incorporates both historical data and on-line signals into the system modeling, and enables the ability to adaptively alter data collection procedures to best capture the critical system features. Collectively, these components lead to a robust and sensitive diagnosis system for wind turbines. This research is strengthened by a close collaboration with industry that will provide abundant historical sensor data and detailed system characterization, and also offer in-field implementation opportunities. The proposed research will have strategic importance on the utilization of wind energy that is currently the most viable clean energy alternative. Today, in the vast areas that have low wind speed, wind energy cannot compete with traditional energy sources as it has a higher cost, mainly owing to its high maintenance costs and low confidence in the diagnosis technology. This dynamic and data-driven fault diagnosis will play a key role in enabling a cost-effective generation of wind electricity. Progress in the fault diagnosis of blades and gearboxes will also benefit the power generation, automobile, aerospace, and engine industries. Meanwhile, the collaborative nature of this research will provide students with a multidisciplinary training and will bring industrial perspective to the universities. This project will have a long-term impact on education through the curriculum development and will promote the public awareness of clean energy concept through outreaches to high schools.