Contingency ranking using neural networks by Radial Basis Function method

Radial Basis Function (RBF) Networks are used for contingency evaluation of bulk power system. The off-line Newton-Raphson load flow calculation are adopted to construct two kinds of performance indexes, Pip (active power performance index) and PIv (reactive power performance index) which reflect th...

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Bibliographic Details
Published in2008 IEEE/PES Transmission and Distribution Conference and Exposition pp. 1 - 4
Main Authors Khazaei, M., Jadid, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2008
Subjects
Artificial neural networks
Load flow
Neural networks
Performance analysis
Power engineering and energy
Power system analysis computing
Power system security
Power systems
Reactive power
Voltage
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Summary:Radial Basis Function (RBF) Networks are used for contingency evaluation of bulk power system. The off-line Newton-Raphson load flow calculation are adopted to construct two kinds of performance indexes, Pip (active power performance index) and PIv (reactive power performance index) which reflect the severity degree of contingencies. The results of off-line load flow calculation are used to train a Radial Basis Function Neural Network for estimating the predefined performance indices. The effectiveness of the purposed method is demonstrated by contingency ranking on IEEE 30-Bus test system. Faster analysis times for contingency ranking are obtained by using the Neural Network.
ISBN:9781424419036
1424419034
ISSN:2160-8555
2160-8563
DOI:10.1109/TDC.2008.4517045