DATA DRIVEN MODELING FOR POWER TRANSFORMER LIFESPAN EVALUATION

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 predic...

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Bibliographic Details
Published in:Journal of systems science and systems engineering 2014-03, Vol.23 (1), p.80-93
Main Authors: Trappey, Charles V., Trappey, Amy J. C., Ma, Lin, Tsao, Wan-Ting
Format: Article
Language:English
Subjects:
Asset management
Complexity
Economic Theory/Quantitative Economics/Mathematical Methods
Energy sources
Engineering
Forecasting
Life span
Logistic回归
Maintenance
Operations Research/Decision Theory
Real time
Remote sensors
Statistical analysis
Supply chains
Transformers
Weibull distribution
Weibull分布
大型电力变压器
实时监控
寿命评估
建模
数据驱动
资产管理
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Summary: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.
ISSN:1004-3756
1861-9576
DOI:10.1007/s11518-014-5227-z