Abstract
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Article Information:
On-line Transient Stability Assessment through Generator Rotor Angles Prediction Using Radial Basis Function Neural Network
Shahbaz A. Siddiqui, Kusum Verma, K.R. Niazi and Manoj Fozdar
Corresponding Author: Shahbaz A. Siddiqui
Submitted: June 14, 2014
Accepted: July 19, 2014
Published: October 10, 2014 |
Abstract:
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On-line Transient Stability Assessment (TSA) is challenging task due to the large number of variables involved and continuously varying operating conditions. This study proposes an on-line transient stability assessment methodology based on the predicted values of generator rotor angles under varying operating conditions for predefined contingency set through Radial Basis Function Neural Network (RBFNN). The real and reactive power loads are taken as input features for training of the neural network. Principal Component Analysis (PCA) is used for dimensionality reduction of the input data set to select informative features. The proposed method is tested on IEEE-39 bus test system and the results obtained for transient stability assessment through predicted rotor angles are promising.
Key words: Artificial neural network, , feature selection,, on-line power system transient stability, , principal component analysis, radial basis function, ,
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Cite this Reference:
Shahbaz A. Siddiqui, Kusum Verma, K.R. Niazi and Manoj Fozdar, . On-line Transient Stability Assessment through Generator Rotor Angles Prediction Using Radial Basis Function Neural Network. Research Journal of Applied Sciences, Engineering and Technology, (14): 1665-1672.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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