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Visualization and analysis of voltage stability using self-organizing neural networks

  • Part VII: Prediction, Forecasting and Monitoring
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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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.

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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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

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  • DOI: https://doi.org/10.1007/BFb0020302

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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