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CHARACTERIZATION OF STATE ESTIMATION BIASES

Published online by Cambridge University Press:  12 December 2005

A. P. Sakis Meliopoulos  and
George K. Stefopoulos
A. P. Sakis Meliopoulos
Affiliation:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, E-mail: sakis.meliopoulos@ece.gatech.edu
George K. Stefopoulos
Affiliation:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, E-mail: gstefop@ece.gatech.edu
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Abstract

The control and operation of an electric power system is based on the ability to determine the state of the system in real time. State estimation (SE) has been introduced in the 1960s to achieve this objective. The initial implementation was based on single-phase measurements and a power system model that was assumed to operate under single-frequency, balanced conditions, and a symmetric system model. These assumptions are still prevalent today. The single-frequency, balanced, and symmetric system assumptions have simplified the implementation but have generated practical problems. The experience is that the SE problem does not have 100% performance; that is, there are cases and time periods for which the SE algorithm will not converge. There are practical and theoretical reasons for this and they are explained in the paper. Recent mergers and mandated regional transmission organizations (RTOs) as well as recent announcements for the formation of mega-RTOs will result in the application of the SE in systems of unprecedented size. We believe that these practical and theoretical issues will become of greater importance. There are scientists who believe that the SE problem is scalable, meaning that it will work for the mega-RTOs the same way that it performs now for medium–large systems. There are scientists who believe that this is not true. The fact is that no one has investigated the problem, let alone performed numerical experiments to prove or disprove any claims. This paper identifies a number of issues relative to the SE of mega-RTOs and provides some preliminary results from numerical experiments for the relation between the SE algorithm performance and the power system size.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 20 , Issue 1 , January 2006 , pp. 157 - 174
Copyright
© 2006 Cambridge University Press

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