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Title: Regression Model Forecasting for Time-Skew Problems in Power System State Estimation
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.  more » « less
Award ID(s):
1845523
NSF-PAR ID:
10489999
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
2023 North American Power Symposium (NAPS)
ISSN:
2833-003X
ISBN:
979-8-3503-1509-7
Page Range / eLocation ID:
1 to 6
Subject(s) / Keyword(s):
Forecasting, Regression analysis, State estimation, Time skew.
Format(s):
Medium: X
Location:
Asheville, NC, USA
Sponsoring Org:
National Science Foundation
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