<abstract xmlns="http://www.w3.org/1999/xhtml"><p>A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Two samples of frequency deviation values at all generator buses are obtained. At each generator bus, the maximum frequency deviation within the two samples is extracted. A vector is then constructed from the extracted maximum frequency deviations. The Euclidean norm of the constructed vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using data generated from the New England test system. The scheme successfully predicted the stability status of all two hundred and five disturbance test cases.</p></abstract>

A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Two samples of frequency deviation values at all generator buses are obtained. At each generator bus, the maximum frequency deviation within the two samples is extracted. A vector is then constructed from the extracted maximum frequency deviations. The Euclidean norm of the constructed vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using data generated from the New England test system. The scheme successfully predicted the stability status of all two hundred and five disturbance test cases.

On-line determination of transient stability status using multilayer perceptron neural network

Department of Electrical and Electronic Engineering

Kwame Nkrumah University of Science and Technology

Department of Electrical and Computer Engineering

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as...

On-line determination of transient stability status using multilayer perceptron neural network

Published Online: Mar 07, 2018

Page range: 58 - 64

Received: Dec 14, 2017

DOI: https://doi.org/10.1515/jee-2018-0008

Keywords
power system stability, stability prediction, transient stability, out-of-step, neural network, Euclidean norm

© 2018 Emmanuel Asuming Frimpong et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

On-line determination of transient stability status using multilayer perceptron neural network

Published Online: Mar 07, 2018

Page range: 58 - 64

Received: Dec 14, 2017

DOI: https://doi.org/10.1515/jee-2018-0008

Keywordspower system stability, stability prediction, transient stability, out-of-step, neural network, Euclidean norm

© 2018 Emmanuel Asuming Frimpong et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
power system stability, stability prediction, transient stability, out-of-step, neural network, Euclidean norm