Neural network approach to dynamic voltage stability prediction

Azah Mohamed, G. B. Jasmon

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The application of artificial neural network (ANN) on dynamic voltage stability analysis is presented. Two ANN models have been utilized, in which the first ANN model is used to classify the power system as to whether it is dynamically stable or unstable. Then the second ANN model is used for the dynamically stable system to predict the voltage magnitudes at load busbars. Both ANN models are based on the multiperceptron model, and the training is done using the error back propagation scheme. The training set patterns are generated by carrying out dynamic simulations, using induction motor and constant P-Q load models. This paper highlights the method for selection of the optimum number of training sets so as to minimise the time taken in the ANN learning process. The performance of the ANN models have been tested and shown to give good results.

Original languageEnglish
Pages (from-to)509-523
Number of pages15
JournalElectric Machines and Power Systems
Volume25
Issue number5
Publication statusPublished - 1996

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Voltage control
Neural networks
Busbars
Backpropagation
Induction motors
Computer simulation
Electric potential

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Neural network approach to dynamic voltage stability prediction. / Mohamed, Azah; Jasmon, G. B.

In: Electric Machines and Power Systems, Vol. 25, No. 5, 1996, p. 509-523.

Research output: Contribution to journalArticle

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