The comparison of the improving effects of ULTC and SVC on dynamical voltage stability using neural networks

Author

Köse, Ercan ; Abaci, Kadir ; Aksoy, Saadettin ; Yalçin, Mehmet Ali

Author_Institution

Dept. of Electron. & Comput. Educ., Mersin Univ., Mersin, Turkey

fYear

2010

fDate

20-22 Sept. 2010

Firstpage

1

Lastpage

5

Abstract

In this paper voltage stability is evaluated by both dynamic P-V curves and time-domain simulations, considering the dynamic control effects of a static var compensator (SVC) and under load tap changing (ULTC) transformer. The proposal in this paper is to use ANN to prediction the ULTC tap ration and SVC susseptance of the voltage stabilty of power system. The objective of this paper is the determination of the critical loading points with bifurcation analysis using a neural network and the comparasion of the ULTC and SVC on the dynamical voltage stabilization. The simulation results and prediction values were obtained using the MATLAB/SIMULINK and NeuroXL prediction simulator respectively.

Keywords

neural nets; power engineering computing; power system dynamic stability; power transformers; static VAr compensators; MATLAB/SIMULINK; NeuroXL prediction simulator; SVC susseptance; ULTC tap ration; ULTC transformer; artificial neural networks; bifurcation analysis; critical loading points; dynamic P-V curves; dynamic control effects; dynamical voltage stability; static var compensator; time-domain simulations; under load tap changing; Artificial neural networks; Power system stability; Predictive models; Reactive power; Stability analysis; Static VAr compensators; Voltage control; ANN; SVC; ULTC; Voltage Stabilty;

fLanguage

English

Publisher

ieee

Conference_Titel

Modern Electric Power Systems (MEPS), 2010 Proceedings of the International Symposium

Conference_Location

Wroclaw

Print_ISBN

978-83-921315-7-1

Type

conf

Filename

6007256