Regression Model Forecasting for Time-Skew Problems in Power System State Estimation

被引：0

作者：

Trevorrow, Gavin ^{[1
]}

Zhou, Ning ^{[1
]}

机构：

[1] SUNY Binghamton, Elect & Comp Engn Dept, Vestal, NY 13850 USA

来源：

2023 NORTH AMERICAN POWER SYMPOSIUM, NAPS | 2023年

基金：

美国国家科学基金会;

关键词：

Forecasting; Regression analysis; State estimation; Time skew;

D O I：

10.1109/NAPS58826.2023.10318604

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The negative impact of measurement time skew on the static state estimation of the power grid has been exacerbated by increasing variation of system operating conditions. To mitigate the time skew problem, this paper proposes a regression model forecasting (RMF) method to forecast the time-skewed measurements, along with a confidence interval estimation (CIE) method to determine the weights associated with the forecasted measurements. The proposed RMF-CIE method is compared against several benchmark methods through Monte-Carlo simulation on the IEEE 16-machine, 68-bus model. It was observed that the proposed RMF-CIE consistently achieved more accurate state estimation on average. In addition, it was found that its estimation accuracy increases with the decrease of the skew time and variation levels.

引用

页数：6

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