DocumentCode :
622177
Title :
Static voltage stability analysis using generalized regression neural network
Author :
Mirzaei, Mohammad ; Jasni, J. ; Hizam, H. ; Wahab, Noor Izzri Abdul ; Moazami, Ehsan
Author_Institution :
Dept. of Electr. & Electron., Univ. Putra Malaysia, Serdang, Malaysia
fYear :
2013
fDate :
3-4 June 2013
Firstpage :
391
Lastpage :
396
Abstract :
The ability of power system to maintain steady voltage at all the buses after happening a disturbance from a given initial operation condition is defined the voltage stability in the system. The focus of this paper is on voltage stability monitoring using the generalized regression neural network by improving algorithm. In this paper, to identify load buses and certain operation condition, the static voltage stability method in power systems is presented. Based on load buses, the index of voltage stability is obtained from the voltage equation derived from a two bus network. The proposed methods are tested on the IEEE-14 bus test system.
Keywords :
neural nets; power engineering computing; power system stability; regression analysis; IEEE-14 bus test system; generalized regression neural network; load bus identification; static voltage stability method; voltage stability monitoring; Circuit stability; Equations; Mathematical model; Power system stability; Stability criteria; Training; Generalized regression neural network; K-fold cross-validation method; Thevenin equivalent circuit; Voltage stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Engineering and Optimization Conference (PEOCO), 2013 IEEE 7th International
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-5072-3
Type :
conf
DOI :
10.1109/PEOCO.2013.6564579
Filename :
6564579
Link To Document :
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