An artificial neural network based adaptive power system stabilizer

Author

Zhang, Y. ; Chen, G.P. ; Malik, O.P. ; Hope, G.S.

Author_Institution

Dept. of Electr. & Comput. Eng., Calgary Univ., Alta., Canada

Volume

8

Issue

1

fYear

1993

fDate

3/1/1993 12:00:00 AM

Firstpage

71

Lastpage

77

Abstract

An artificial neural network (ANN)-based power system stabilizer (PSS) and its application to power systems are presented. The ANN-based PSS combines the advantages of self-optimizing pole shifting adaptive control strategy and the quick response of ANN to introduce a new generation PSS. A popular type of ANN, the multilayer perceptron with error backpropagation training method, is used in this PSS. The ANN was trained by the training data group generated by the adaptive power system stabilizer (APSS). During the training, the ANN was required to memorize and simulate the control strategy of APSS until the differences were within the specified criteria. Results show that the proposed ANN-based PSS can provide good damping of the power system over a wide operating range and significantly improve the dynamic performance of the system

Keywords

adaptive control; backpropagation; learning (artificial intelligence); neural nets; power system control; power system stability; adaptive control; adaptive power system stabilizer; artificial neural network; damping; dynamic performance; error backpropagation training method; multilayer perceptron; self-optimizing pole shifting; training; Adaptive control; Adaptive systems; Artificial neural networks; Backpropagation; Multilayer perceptrons; Power generation; Power system dynamics; Power system simulation; Power systems; Training data;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/60.207408

Filename

207408