State estimation for power systems with multilayer perceptron neural networks

Author

Ivanov, Ovidiu ; Garvrilas, M.

Author_Institution

Fac. of Electr. Eng., “Gheorghe Asachi” Tech. Univ. of Iasi, Iasi, Romania

fYear

2012

fDate

20-22 Sept. 2012

Firstpage

243

Lastpage

246

Abstract

The Static state estimation is widely used in power systems for real time monitoring and analysis. Standard methods, such as the weighted least squares (WLS) algorithm, require the computation of bus admittance and Jacobian matrices and the solution is found in an iterative process. This paper presents an alternative for the classic state estimation (SE) algorithms, which uses a multilayer perceptron for the state estimator. Results are presented for the IEEE 14 bus system.

Keywords

IEEE standards; Jacobian matrices; iterative methods; least squares approximations; multilayer perceptrons; power engineering computing; power system state estimation; IEEE 14 bus system; Jacobian matrices; SE; WLS; bus admittance; iterative process; multilayer perceptron neural networks; power systems; state estimation; static state estimation; weighted least squares algorithm; Artificial neural networks; Load flow; Measurement uncertainty; Power measurement; State estimation; Voltage measurement; multilayer perceptron; state estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on

Conference_Location

Belgrade

Print_ISBN

978-1-4673-1569-2

Type

conf

DOI

10.1109/NEUREL.2012.6420026

Filename

6420026