Monitoring multiple harmonic sources in power systems using neural networks

Author

Negnevitsky, M. ; Ringrose, M.

Author_Institution

Univ. of Tasmania, Hobart

fYear

2005

fDate

27-30 June 2005

Firstpage

1

Lastpage

6

Abstract

This paper presents a method for monitoring multiple harmonic sources in a power system using a reduced number of harmonic monitoring stations. Artificial neural networks are used to provide initial estimates of the harmonic sources based on the measured harmonics and fundamental load flows. State estimation is then utilised to improve the estimates. This approach is tested on a simulated power system based on the IEEE 14-bus test system with several harmonic-producing loads. The outlined method can be used to reduce the number of required measurements in many real state estimation problems.

Keywords

neural nets; power engineering computing; power system harmonics; power system state estimation; IEEE 14-bus test system; artificial neural networks; multiple harmonic sources monitoring; power system simulation; real state estimation problems; Artificial neural networks; Frequency estimation; Load flow; Monitoring; Neural networks; Power system harmonics; Power system measurements; Power system simulation; State estimation; System testing; Artificial Neural Networks; Harmonic Sources; Monitoring; State Estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Tech, 2005 IEEE Russia

Conference_Location

St. Petersburg

Print_ISBN

978-5-93208-034-4

Electronic_ISBN

978-5-93208-034-4

Type

conf

DOI

10.1109/PTC.2005.4524736

Filename

4524736