Title :
Online voltage stability contingency selection using improved RSI method based on ANN solution
Author :
Zhang, Y. ; Zhou, Z.
Author_Institution :
RTDS Technol. Inc., Winnipeg, Man., Canada
Abstract :
This paper proposed an efficient methodology for voltage stability contingency selection, named improved RSI method based on ANN solution. The basic idea of the reactive support index (RSI) method is adopted, while the artificial neural network (ANN) solution is employed to handle the nonlinear relationship between the RSI and the voltage stability margin variation. The improved methodology combines the advantages of its clear physical meaning from the RSI method and its high accuracy from using the ANN. The method is tested on the IEEE 39 buses test system. Numerical studies illustrate that the new method has good performance on both the accuracy and speed.
Keywords :
neural nets; power system analysis computing; power system dynamic stability; ANN; IEEE 39 buses test system; improved RSI method; improved reactive support index method; online voltage stability contingency selection; voltage stability margin; Artificial neural networks; Drives; Neural networks; Power engineering computing; Power generation; Power system planning; Power system stability; Reactive power; System testing; Voltage control;
Conference_Titel :
Power Engineering Society Winter Meeting, 2002. IEEE
Print_ISBN :
0-7803-7322-7
DOI :
10.1109/PESW.2002.985134