Fast linear programming state estimation using the dual formulation

The authors present a fast and efficient technique for solving the power system state estimation problem using linear programming (LP). The dual formulation of the original problem is described. This formulation improves the solution time significantly. Four LP state estimators are presented and test results on the IEEE 30, 57, and 118 bus standard systems are included. Both the full and decoupled dual problems were solved. Test results indicate that the dual estimators are superior to the primal estimators. >

[1]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

[2]  K. Lo,et al.  A Decoupled Linear Programming Technique for Power System State Estimation , 1986, IEEE Power Engineering Review.

[3]  Felix F. Wu,et al.  Mutiple Bad Data Identwication for State Estimation by Combinatorial Oftimization , 1986, IEEE Transactions on Power Delivery.

[4]  Nabih N. Abdelmalek.  An efficient method for the discrete linear ₁ approximation problem , 1975 .

[5]  A. Monticelli,et al.  Fast Decoupled State Estimation and Bad Data Processing , 1979, IEEE Transactions on Power Apparatus and Systems.

[6]  M. R. Irving,et al.  Power System State Estimation Using Linear Programming , 1978 .

[7]  A. Brameller,et al.  Power System Tracking State Estimation and Bad Data Processing , 1982, IEEE Transactions on Power Apparatus and Systems.

[8]  H. M. Wagner.  Linear Programming Techniques for Regression Analysis , 1959 .

[9]  A. Sasson,et al.  A Fast and Reliable State Estimation Algorithm for AEP's New Control Center , 1982, IEEE Transactions on Power Apparatus and Systems.

[10]  John Peschon,et al.  State Estimation in Power Systems Part I: Theory and Feasibility , 1970 .

[11]  Ali Abur,et al.  A bad data identification method for linear programming state estimation , 1990 .

[12]  A.A. El-Keib,et al.  Linear programming based state estimation in power systems , 1990, [1990] Proceedings. The Twenty-Second Southeastern Symposium on System Theory.

[13]  I. Barrodale,et al.  An Improved Algorithm for Discrete $l_1 $ Linear Approximation , 1973 .

[14]  M. Vidyasagar,et al.  Bad Data Rejection Properties of Weughted Least Absolute Value Techniques Applied to Static State Estimation , 1982, IEEE Transactions on Power Apparatus and Systems.

[15]  E. Handschin,et al.  Bad data analysis for power system state estimation , 1975, IEEE Transactions on Power Apparatus and Systems.

[16]  D. Falcão,et al.  Linear programming state estimation: error analysis and gross error identification , 1988 .

[17]  A.A. El-Keib,et al.  A model decoupled state estimator with effective bad data identification , 1989, Proceedings. IEEE Energy and Information Technologies in the Southeast'.

[18]  Lamine Mili,et al.  Implementation of the hypothesis testing identification in power system state estimation , 1988 .

[19]  O. Alsac,et al.  Fast Decoupled Load Flow , 1974 .