DATA DRIVEN MODELING FOR POWER TRANSFORMER LIFESPAN EVALUATION  

作　　者：Charles V.TRAPPEY Amy J.C.TRAPPEY Lin MA Wan-Ting TSAO 

机构地区：[1]Department of Management Science,National Chiao Tung University [2]Department of Industrial Engineering and Engineering Management,National Tsing Hua University [3]School of Chemistry,Physics and Mechanical Engineering,Queensland University of Technology

出　　处：《Journal of Systems Science and Systems Engineering》2014年第1期80-93,共14页系统科学与系统工程学报（英文版）

基　　金：the research support granted by Taiwan's National Science Council and the Australian Government's Cooperative Research Centers Program

摘　　要：Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation＇s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit （RTU） system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation＇s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit （RTU） system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

关 键 词：Condition based maintenance （CBM） prognostics and health management （PHM） LOGISTICREGRESSION remaining life prediction sustainable engineering asset management 

分 类 号：TM41[电气工程—电器]