A BAD DATA IDENTIFICATION METHOD FOR LINEAR-PROGRAMMING STATE ESTIMATION

被引：59
|
作者：
ABUR, A
机构：
[1] Texas A&M University, Department of Electrical Engineering, Texas, 77843, College Station
来源：
IEEE TRANSACTIONS ON POWER SYSTEMS | 1990年 / 5卷 / 03期
关键词：
D O I：
10.1109/59.65919
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents a bad data identification procedure for linear programming (LP) power system static state estimation. LP state estimators minimize the weighted sum of the absolute values of the measurement residuals. The presented procedure first detects the bad data using the measurement residuals of those measurements rejected by the LP estimator. Then the bad measurement is identified and eliminated by estimating the measurement errors of the zero residual measurements. The residuals obtained from this second estimation step are made use of for this purpose. In order to minimize the computational burden during the elimination cycles, a fast way of eliminating measurements through weight changing is also presented. The performance of the proposed procedure is tested and the results are presented, using AEP's 14, 30, 57 and 118 bus power systems. © 1990 IEEE
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页码：894 / 901
页数：8
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