A neuro-fuzzy power system stabiliser

Author

Hosseinzadeh, N. ; Kalam, A.

Author_Institution

Dept. of Electr. & Electron. Eng., Victoria Univ. of Technol., Melbourne, Vic., Australia

Volume

1

fYear

1996

fDate

5-10 Aug 1996

Firstpage

608

Abstract

A fuzzy logic power system stabiliser has been developed using speed and active power deviations as the controller input variables. The inference mechanism of the fuzzy logic controller is represented by a 7×7 decision table, i.e. 49 if-then rules. In order to use it under a wide range of operating conditions, its parameters have been tuned using a neural network. The tuned stabiliser has been tested by performing nonlinear simulations using a synchronous machine-infinite bus model. It is shown that the neuro-fuzzy stabiliser is superior to a fixed parameter fuzzy logic power system stabiliser

Keywords

fuzzy control; knowledge based systems; neurocontrollers; power system control; power system stability; 7×7 decision table; active power deviations; controller input variables; fuzzy logic controller; if-then rules; inference mechanism; neural network; neuro-fuzzy power system stabiliser; nonlinear simulations; parameter tuning; rule based fuzzy logic PSS; speed deviations; synchronous machine-infinite bus model; tuned stabiliser; Control systems; Fuzzy logic; Inference mechanisms; Input variables; Neural networks; Performance evaluation; Power system modeling; Power system simulation; Power systems; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, Control, and Instrumentation, 1996., Proceedings of the 1996 IEEE IECON 22nd International Conference on

Conference_Location

Taipei

Print_ISBN

0-7803-2775-6

Type

conf

DOI

10.1109/IECON.1996.571026

Filename

571026