Application of an inverse input/output mapped ANN as a power system stabilizer

Author

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

Author_Institution

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

Volume

9

Issue

3

fYear

1994

fDate

9/1/1994 12:00:00 AM

Firstpage

433

Lastpage

441

Abstract

An artificial neural network (ANN), trained as an inverse of the controlled plant, to function as a power system stabilizer (PSS) is presented in this paper. In order to make the proposed ANN PSS work properly, it was trained over the full working range of the generating unit with a large variety of disturbances. Data used to train the ANN PSS consisted of the control input and the synchronous machine response with an adaptive PSS (APSS) controlling the generator. During training, the ANN was required to memorize the reverse input/output mapping of the synchronous machine. After the training, the output of the synchronous machine was applied as the input of the ANN PSS and the output of the ANN PSS was used as the control signal. Simulation results show that the proposed ANN PSS can provide good damping of the power system over a wide operating range and significantly improve the system performance

Keywords

adaptive control; learning (artificial intelligence); neural nets; power system analysis computing; power system computer control; power system stability; synchronous machines; artificial neural network; control input; control signal; damping; generating unit; generator control; inverse input/output mapped ANN; power system stabilizer; reverse input/output mapping; simulation; synchronous machine response; training; Adaptive control; Artificial neural networks; Control systems; Damping; Power system simulation; Power systems; Programmable control; Synchronous generators; Synchronous machines; System performance;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/60.326460

Filename

326460