Application of the ARTMAP neural network to power system stability studies

Author

Assadi, H. ; Tan, A. ; Etezadi-Amoli, M. ; Egbert, D. ; Fadali, M.S.

Author_Institution

Dept. of Electr. Eng., Nevada Univ., Reno, NV, USA

fYear

1992

Firstpage

1080

Abstract

The authors discuss the application of a novel variation of the adaptive resonance theory (ART) neural network called fuzzy ARTMAP to the determination of the steady-state stability of a synchronous generator. The model of the generator includes the voltage regulator, the excitor, and the power system stabilizer. The results obtained with the fuzzy ARTMAP network are compared with those obtained with a backpropagation network. For online training, the fuzzy ARTMAP network was found to be a better choice because of its faster convergence, but in some cases, the fuzzy ARTMAP network did not perform as well as the BP network

Keywords

backpropagation; exciters; neural nets; power system computer control; power system stability; synchronous generators; voltage control; adaptive resonance theory; backpropagation; excitor; fuzzy ARTMAP network; neural network; power system stability; synchronous generator; voltage regulator; Fuzzy neural networks; Neural networks; Power generation; Power system modeling; Power system stability; Resonance; Steady-state; Subspace constraints; Synchronous generators; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1992., IEEE International Conference on

Conference_Location

Chicago, IL

Print_ISBN

0-7803-0720-8

Type

conf

DOI

10.1109/ICSMC.1992.271646

Filename

271646