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Title: A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

Abstract

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADA and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.

Authors:
 [1];  [2];  [1];  [2];  [2];  [2]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Falls Church, VA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1420442
Report Number(s):
PNNL-SA-127646
Journal ID: ISSN 0885-8950
Grant/Contract Number:  
GMLC0070; 1711191; AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: PP; Journal Issue: 99; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; power system state estimation; measurement correlations; robust estimation; phasor measurement units; vector auto-regression model; bad data; generalized regression model; imperfect time synchronization

Citation Formats

Zhao, Junbo, Wang, Shaobu, Mili, Lamine, Amidan, Brett, Huang, Renke, and Huang, Zhenyu. A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization. United States: N. p., 2018. Web. doi:10.1109/TPWRS.2018.2790390.
Zhao, Junbo, Wang, Shaobu, Mili, Lamine, Amidan, Brett, Huang, Renke, & Huang, Zhenyu. A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization. United States. doi:10.1109/TPWRS.2018.2790390.
Zhao, Junbo, Wang, Shaobu, Mili, Lamine, Amidan, Brett, Huang, Renke, and Huang, Zhenyu. Mon . "A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization". United States. doi:10.1109/TPWRS.2018.2790390. https://www.osti.gov/servlets/purl/1420442.
@article{osti_1420442,
title = {A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization},
author = {Zhao, Junbo and Wang, Shaobu and Mili, Lamine and Amidan, Brett and Huang, Renke and Huang, Zhenyu},
abstractNote = {Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADA and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.},
doi = {10.1109/TPWRS.2018.2790390},
journal = {IEEE Transactions on Power Systems},
issn = {0885-8950},
number = 99,
volume = PP,
place = {United States},
year = {2018},
month = {1}
}

Journal Article:
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Cited by: 5 works
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Figures / Tables:

Fig. 1 Fig. 1: MAE of the alternatives A1 through A6 for the IEEE 30-bus system, where 20% randomly chosen buses are deployed with the PMUs; (a) voltage magnitude; (b) voltage angle.

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.