A Data-driven Method for Estimating Parameter Uncertainty in PMU-based Power Plant Model Validation

被引：0
|
作者：
Eisenbarth, Jacob [1 ]
Wold, Josh [1 ]
机构：
[1] Montana Technol Univ, Butte, MT 59701 USA
来源：
2020 IEEE KANSAS POWER AND ENERGY CONFERENCE (KPEC) | 2020年
关键词：
Bootstrapping; Model Validation; Monte Carlo; Parameter Estimation and Uncertainty;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Event-based, online power plant model validation is an important component in economical maintenance of power grid reliability. It is well-known that any given event will not excite all portions of a power plant model, so those portions cannot be considered validated. This paper introduces a method to provide quantitative information about the uncertainty of model parameters for a given event. This information can be used to identify the portions of a model that should be considered validated and those that will require additional events to validate.
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页数：6
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