State Estimation Accuracy of Tuned Least Measurement Rejected Estimator

Abstract

Weighted Least Square (WLS) and Weighted Least Absolute Value (WLAV) estimators are most commonly employed in electric power industry. WLS fails in the presence of bad data and WLAV estimator has large computational burden. Least Measurement Rejected (LMR) is a robust estimator which can handle bad data efficiently and has lower computational burden. LMR estimator has an important tolerance parameter which is used to reject bad data during estimation process. In this paper, an iterative tuning approach for the tolerance parameter of LMR has been proposed. The accuracy and computational efficiency of the proposed approach have been compared with most commonly used WLS and WLAV estimators. The accuracy has been computed in terms of Cumulative Estimation Error (CEE) indicator for IEEE-30 and IEEE-118 bus systems.