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Artificial Neural Network Based Method to Mitigate Temporary Over-voltages

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Author(s): Iman Sadeghkhani | Abbas Ketabi | Rene Feuillet

Journal: Majlesi Journal of Electrical Engineering
ISSN 2008-1413

Volume: 5;
Issue: 3;
Start page: 15;
Date: 2011;
Original page

Keywords: Artificial neural networks | Equivalent circuit | Harmonic index | Temporary over-voltages | Inrush currents | Power system restoration | Transformer energization.

ABSTRACT
Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching over-voltages. The most effective method for the limitation of the switching over-voltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch.‎ We introduce a harmonic index that its minimum value is corresponding to the best-case switching time.‎ Also, this paper ‎presents an Artificial Neural Network (ANN)-based approach to ‎estimate the optimum switching instants for real time applications. In the proposed ANN, second order Levenberg–Marquardt ‎ method is used to train the multilayer perceptron. ANN training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.
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