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

Abstract

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 PMU measurements are calculated separately through unscented transformation and a vector autoregression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model. 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 of synchronization issues between SCADA and PMU measurements, either forecasted PMU measurements or prior SCADA measurements from the last estimation run are leveraged to restore system observability. After that, robust generalized maximum-likelihood (GM)-estimator is developed to integrate full measurement correlations and handle bad data in SCADA and PMU measurements. Thanks to the robustness and flexibility of the proposed robust estimator, bad data as well as imperfect measurement synchronization are mitigated, yielding high statistical efficiency. Comprehensive comparison results with other alternatives under various conditions demonstrate the benefits of the proposed framework.

Authors:
ORCiD logo; ORCiD logo; ORCiD logo; ; ORCiD logo;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1490196
Report Number(s):
PNNL-SA-127646
Journal ID: ISSN 0885-8950
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 33; Journal Issue: 4; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English

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. Sun . "A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization". United States. doi:10.1109/TPWRS.2018.2790390.
@article{osti_1490196,
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 = {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 PMU measurements are calculated separately through unscented transformation and a vector autoregression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model. 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 of synchronization issues between SCADA and PMU measurements, either forecasted PMU measurements or prior SCADA measurements from the last estimation run are leveraged to restore system observability. After that, robust generalized maximum-likelihood (GM)-estimator is developed to integrate full measurement correlations and handle bad data in SCADA and PMU measurements. Thanks to the robustness and flexibility of the proposed robust estimator, bad data as well as imperfect measurement synchronization are mitigated, yielding high statistical efficiency. Comprehensive comparison results 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 = 4,
volume = 33,
place = {United States},
year = {2018},
month = {7}
}