Auto tuning of measurement weights in WLS state estimation

This paper describes an approach for choosing and updating measurement weights used in weighted least squares (WLS) state estimation. Since the weights are related to the measurement error variances, sample variances are estimated using historical data from previous measurement scans and the corresp...

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Bibliographic Details
Published inIEEE transactions on power systems Vol. 19; no. 4; pp. 2006 - 2013
Main Authors Shan Zhong, Abur, A.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Auto tuning
Automotive components
Calibration
Equations
Error analysis
Error correction
Estimators
Least squares approximation
Least squares method
Measurement errors
measurement weights
On-line systems
Power system measurements
Power system simulation
power system state estimation
random error variances
Simulation
State estimation
Testing
Tuning
Weight measurement
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Summary:This paper describes an approach for choosing and updating measurement weights used in weighted least squares (WLS) state estimation. Since the weights are related to the measurement error variances, sample variances are estimated using historical data from previous measurement scans and the corresponding WLS estimation results. The proposed approach can be implemented as a one-time estimation function for off-line execution or as a recursive function for updating the measurement weights on-line. Simulated measurement data and state estimation results are used to test and verify the accuracy of the proposed method. The proposed method can be integrated into an existing WLS state estimator as an added feature.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2004.836182