Title :
Auto tuning of measurement weights in WLS state estimation
Author :
Zhong, Shan ; Abur, Ali
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
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.
Keywords :
least squares approximations; power system state estimation; tuning; weighing; autotuning; error variance measurement; weight measurement; weighted least squares state estimation; Calibration; Equations; Error correction; Least squares approximation; Measurement errors; Power system measurements; Power system simulation; State estimation; Testing; Weight measurement; 65; Auto tuning; measurement weights; power system state estimation; random error variances;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2004.836182