A hybrid model for transient stability evaluation of interconnected longitudinal power systems using neural network/pattern recognition approach

Author

Chang, C.S. ; Srinivasan, Dipti ; Liew, A.C., Sr.

Author_Institution

Dept. of Electr. Eng., Singapore Polytech., Singapore

Volume

9

Issue

1

fYear

1994

fDate

2/1/1994 12:00:00 AM

Firstpage

85

Lastpage

92

Abstract

A methodology for evaluation of transient stability of medium size interconnected longitudinal power systems has been developed using a hybrid neural network pattern recognition approach. Assessment of transient stability is done using a fast pattern recognition algorithm at each load level, accurately predicted by a neural network on a half-hourly basis. As opposed to the conventional approaches, this hybrid strategy can make fast decisions with less computations

Keywords

feedforward neural nets; load forecasting; pattern recognition; power system analysis computing; power system interconnection; power system stability; power system transients; feedforward neural nets; hybrid model; interconnected longitudinal power systems; load forecasting; neural network; pattern recognition; security transfer limits; transient stability evaluation; Hybrid power systems; Load forecasting; Neural networks; Pattern recognition; Power system interconnection; Power system modeling; Power system security; Power system stability; Power system transients; Weather forecasting;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.317554

Filename

317554