Analysis of SSR using artificial neural networks [power system simulation]

Author

Nagabhushana, B.S. ; Chandrasekharaiah, H.S.

Author_Institution

Dept. of High Voltage Eng., Indian Inst. of Sci., Bangalore, India

fYear

1996

Firstpage

416

Lastpage

420

Abstract

Artificial neural networks (ANNs) are being advantageously applied to power system analysis problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance (SSR) analysis is presented. The designed ANN outputs a measure of the possibility of the occurrence of SSR and is fully trained to accommodate the variations of power system parameters over the entire operating range. The effectiveness of this approach is tested by experimenting on the first bench mark model proposed by IEEE Task Force on SSR

Keywords

eigenvalues and eigenfunctions; neural nets; power system analysis computing; power system stability; subsynchronous resonance; SSR; artificial neural networks; computer simulation; eigenvalue analysis; input-output mappings; learning process; natural frequencies; operating range; parameters; power systems; subsynchronous resonance; Artificial neural networks; Capacitance; Capacitors; Inductors; Neural networks; Power systems; Reactive power; Rotors; Static VAr compensators; Thyristors;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-3115-X

Type

conf

DOI

10.1109/ISAP.1996.501109

Filename

501109