Abstract
On the basis of a compelling mathematical description of voltage stability in electrical power systems and its indication using the minimum singular value of the load flow Jacobian the application of a self-organizing Kohonen-Neural-Network (KNN) is presented for a fast and secure indication and visualization of voltage stability. The advantage of the structural representation of the system condition by the KNN is worked out bypassing the disabilities of standard voltage stability indicators. In addition the application of KNN aims at the analysis of measures for the improvement of voltage stability. All examples are calculated using a model of a real power transmission system.
Preview
Unable to display preview. Download preview PDF.
References
Prieto Schmidt, H.; Adams, R. N.: “Assessment of StaticVoltage Stability Using Artificial Neural Networks”Proc. of 11th Power System Computation Conference, 1993
Mori, H.; Tamaru, Y.; Tsuzuki, S.:“An Artificial Neural-Net Based Technique for Power System Dynamic Stability with Kohonen Model”, IEEE Transactions on Power Systems, Vol. 7, No. 2, 1992
Ajjarapu, V; Lee, B.: “Bifurcation Flow: A Tool to Study Both Static and Dynamic Aspects of Voltage Stability”, Proc. of Bulk Power System Voltage Phenomena III, Davos, 1994
Thomas, R. J.; Tiranuchit, A.: “A Posturing Strategy against Voltage Instabilities in Electric Power Systems”, IEEE Transactions on Power Systems, Vol. 3, No. 1, p. 87, 1988
Hui, K. C.; Short, M. J.: “A Novel Matrix Nearness Approach For Fast Voltage Collapse Proximity Analysis”, Proc. of 11th Power System Computation Conference, 1993
Dobson, I.; Alvarado, F.; Hu, Y.: “Computation of Closest Bifurcations in Power Systems”, IEEE Transactions on Power Systems, Vol. 9, No. 2, May 1994
Garber, T.: “Entwicklung eines Indikators zur Bestimmung der Spannungsstabilität mit Hilfe von künstlichen neuronalen Netzen”, Technical Report, Department of Electrical Power Engineering, University of Dortmund, 1995
Dillon, T. S.; Niebur, D.: “Neural Network Applications in Power Systems”, CRL Publishing Ltd., Leics, UK, 1996
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Handschin, E., Kuhlmann, D., Rehtanz, C. (1997). Visualization and analysis of voltage stability using self-organizing neural networks. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020302
Download citation
DOI: https://doi.org/10.1007/BFb0020302
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63631-1
Online ISBN: 978-3-540-69620-9
eBook Packages: Springer Book Archive