Automatic contingency grouping using partial least squares and feed forward neural network technologies applied to the static security assessment problem

Automatic contingency grouping using partial least squares and feed forward neural network technologies applied to the static security assessment problem

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...

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Bibliographische Detailangaben
Hauptverfasser: Fischer, D., Szabados, B., Poehlman, S.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Feedforward neural networks
Feeds
Least squares methods
Neural networks
Power system modeling
Power system security
Power system simulation
Predictive models
Reactive power
Voltage
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Beschreibung
Zusammenfassung: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.
DOI:10.1109/LESCPE.2003.1204684