Zongchang Liu
NSF I/UCRC Center for Intelligent Maintenance System (IMS) University of Cincinnati, USA
Title: Workshop on A Tutorial for Wind Farm Intelligent Prognostics and Health Management
Biography
Biography: Zongchang Liu
Abstract
The emerging wind energy market has been growing exponentially during the past decade. As the number of wind turbines increases rapidly, there are fast-growing concerns for their maintenance and health management. Prognostics of turbine performance degradation and incipient faults in critical components can thus offer improvements in availablity of wind turbines by enabling predictive maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring system (CMS) have been widely adopted for such purpose. This paper provides a systematic framework for data-driven health prognostics of wind turbine, together with detailed analysis for different health modeling approaches adopted to various subsystems. Degradation asessment for turbine efficiency and incipient fault detection for drivetrain components will be highlighted. A Cyber-physical system architecture is further propsed to integrate data analytics, decision support, and maintenance execution to adapt to big data environment of turbine fleets. Demonatration and implementation process of the proposed system on National Instruments LabVIEW platfrom and Watchdog Agent Toolkit are also provided in the case study section.