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.

引用

页数：6

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