Neuro computing for state estimation in power systems

Research output: Contribution to journalConference article

Abstract

This paper presents a new method for QP-based power system state estimation using an artificial neural network. QP-based state estimation makes use of the fact that the observation equation is expressed as a set of exact quadratic equations in rectangular coordinate. The problem formulation is transformed into evaluating equilibriums of the differential equations with an artificial neural network that is based on the Lagrange function. Thus, the obtained solution is optimal in a sense of the Kuhn-Tucker conditions.

Original languageEnglish
Pages (from-to)69-72
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
VolumeSuppl
Publication statusPublished - 1 Jan 1996
EventProceedings of the 1996 IEEE International Symposium on Circuits and Systems, ISCAS. Part 1 (of 4) - Atlanta, GA, USA
Duration: 12 May 199615 May 1996

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