Title :
Contingency ranking using neural networks by Radial Basis Function method
Author :
Khazaei, Mohammad ; Jadid, Shahram
Author_Institution :
Pars Oil & Gas Co., Tehran
Abstract :
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.
Keywords :
power engineering computing; radial basis function networks; reactive power; active power performance index; contingency ranking; neural networks; radial basis function method; reactive power performance index; 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;
Conference_Titel :
Transmission and Distribution Conference and Exposition, 2008. T&D. IEEE/PES
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4244-1903-6
Electronic_ISBN :
978-1-4244-1904-3
DOI :
10.1109/TDC.2008.4517045
