Title :
Automatic contingency grouping using partial least squares and feed forward neural network technologies applied to the static security assessment problem
Author :
Fischer, Daniel ; Szabados, Barna ; Poehlman, S.
Author_Institution :
Kinectrics, Toronto, Ont., Canada
Abstract :
The paper shows how a number of feed forward back propagation neural networks can be trained to predict power system bus voltages after a contingency. The approach is designed to use very few learning examples. thus being suitable for on-line use. The method was applied to the 10-machine, 39-bus New England Power System model.
Keywords :
backpropagation; feedforward neural nets; least squares approximations; power system analysis computing; power system dynamic stability; power system security; 10-machine 39-bus New England Power System model; automatic contingency grouping; feed forward back propagation neural networks; feed forward neural network technologies; partial least squares; power system bus voltages prediction; power system contingency; static security assessment; voltage stability; Feedforward neural networks; Feeds; Least squares methods; Neural networks; Power system modeling; Power system security; Power system simulation; Predictive models; Reactive power; Voltage;
Conference_Titel :
Power Engineering, 2003 Large Engineering Systems Conference on
Print_ISBN :
0-7803-7863-6
DOI :
10.1109/LESCPE.2003.1204684
