Artificial Neural Network Based Method to Mitigate Temporary Over-voltages

Sadeghkhani, Iman; Ketabi, Abbas; Feuillet, Rene

Document Type : Review Article

Authors

University of Kashan

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 it’s 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, Levenberg–Marquardt ‎second order 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.

Keywords

References

[1] M.M. Adibi, R.J. Kafka, “Power System Restoration Issues,” IEEE Computer Application in power, vol. 4, pp.19-24, 1991.

[2] M.M. Adibi, R.W. Alexander, and B. Avramovic, “Overvoltage control during restoration,” IEEE Trans. Power Syst., vol. 7, pp. 1464–1470, Nov. 1992.

[3] A. Ketabi, A.M. Ranjbar, and R. Feuillet, “Analysis and Control of Temporary Over-voltages for Automated Restoration Planning,” IEEE Trans. Power Delivery, vol. 17, pp. 1121–1127, Oct. 2002.

[4] G. Morin, “Service restoration following a major failure on the hydroquebec power system,” IEEE Trans. Power Delivery, vol. 2, pp. 454–463, Apr. 1987.

[5] O. Bourgault, G. Morin, “Analysis of harmonic overvoltage due to transformer saturation following load shedding on hydro-quebec-NYPA 765 kV interconnection,” IEEE Trans. Power Delivery, vol. 5, pp. 397–405, Jan. 1990.

[6] I. Sadeghkhani, “Using Artificial Neural Network for Estimation of Switching and Resonance Over-voltages during Bulk Power System Restoration,” M.Sc. Dissertation, Department of Electrical Engineering, University of Kashan, 2009.

[7] G. Sybille, M. M. Gavrilovic, J. Belanger, and V. Q. Do, “Transformer saturation effects on EHV system over-voltages,” IEEE Trans. Power App. Syst., vol. PAS-104, pp. 671–680, Mar. 1985.

[8] I. Sadeghkhani, A. Ketabi, and R. Feuillet, “Estimation of Temporary Over-voltages during Power System Restoration using Artificial Neural Network,” in Proc. 15th International Conference on Intelligent System Applications to Power Systems, Curitiba, Brazil, Nov. 2009.

[9] I. Sadeghkhani, A. Ketabi, and R. Feuillet, “New Approach to Harmonic Over-voltages Reduction during Transformer Energization via Controlled Switching,” in Proc. 15th International Conference on Intelligent System Applications to Power Systems, Curitiba, Brazil, Nov. 2009.

[10] J.H. Brunke, K.J. Fröhlich, “Elimination of Transformer Inrush Currents by Controlled Switching–Part I: Theoretical Considerations,” IEEE Trans. on Power Delivery, vol. 16, pp. 276 – 280, April 2001.

[11] K. Dantas, D. Fernandes, L.A. Neves, B.A. Souza, L.‎ Fonseca, “Mitigation of switching over-voltages in transmission lines via controlled switching,” Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, ‎‎2008 IEEE, 20-24 July 2008, pp. 1-8.

[12] D. Thukaram, H.P. Khincha, Sulabh Khandelwal, “Estimation of switching transient peak over-voltages during transmission line energization using artificial neural network,” Electric Power System Research, vol.76, pp. 259–269, 2006.

[13] M.T. Hagan, M.B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Trans. Neural Network, vol.5, pp. 989-993, Nov. 1994.

[14] T. Hayashi et al., “Modeling and simulation of black start and restoration of an electric power system. Results of a questionnaire,” Electra, no. 131, pp. 157–169, July 1990.

[15] L.H. Fink, K.L. Liou, C.C. Liu, “From generic restoration actions to specific restoration strategies,” IEEE Trans. Power Syst., vol. 10, pp. 745–752, May 1995.

[16] G. Sybille, P. Brunelle, L. Hoang, L. A. Dessaint, and K. Al-Haddad, “Theory and applications of power system blockset, a MATLAB/Simulink-based simulation tool for power systems,” in Proc. IEEE Power Eng. Soc. Winter Meeting, pp. 774–779.

[17] M.M. Duro, “Damping Modelling in Transformer Energization Studies for System Restoration: Some Standard Models Compared to Field Measurements,” in Proc. IEEE Bucharest Power Tech Conference, Bucharest, Romania, June 2009.

[18] P.G. Boliaris, J.M. Prousalidis, N.D. Hatziargyriou, and B. C. Papadias, “Simulation of long transmission lines energization for black start studies,” in Proc. 7th Mediterranean Electrotechn.Conf., pp. 1093–1096, 1994.

[19] S. Haykin, Neural Network: A Comprehensive Foundation, 2nd ed., Prentice Hall, 1998.

[20] S. Wunderlich, M.M. Adibi, R. Fischl, and C.O.D. Nwankpa, “An approach to standing phase angle reduction,” IEEE Trans. Power Syst., vol. 9, pp. 470–478, Feb. 1994.

Majlesi Journal of Electrical Engineering

Artificial Neural Network Based Method to Mitigate Temporary Over-voltages

References

References

Volume 5, Issue 3 - Serial Number 3
September 2011

Artificial Neural Network Based Method to Mitigate Temporary Over-voltages

References

References

Volume 5, Issue 3 - Serial Number 3September 2011

Volume 5, Issue 3 - Serial Number 3
September 2011