Auto tuning of measurement weights in WLS state estimation

• Published in: IEEE transactions on power systems Vol. 19; no. 4; pp. 2006 - 2013
• Main Authors: Shan Zhong, Abur, A.
• Format: Journal Article
• Language: English
• 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
• DOI: 10.1109/TPWRS.2004.836182
• ISSN: 0885-8950

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.