Optimum Parameter Identification Technique of Metal Oxide Surge Arrester Model Using Genetic Algorithm
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Metal oxide arresters have dynamic characteristics that are significant for overvoltage coordination studies involving fast front surges. This dynamic responses are frequency-dependent and can not be represented with the conventional non-linear V-I characteristics. Several models have been proposed to simulate this frequency-dependent behavior. But their accuracy are not acceptable in some cases. In this paper a new technique based on the genetic algorithm is proposed to obtain the best possible parameter values of the metal oxide arrester model. The existing GA method is developed by adding a weight term in the objective function which is related to the magnitude of input impulse current in time domain. The proposed method is applied on recommended surge arrester dynamic model from the IEEE W.G. 3.4.11 and results are obtained. The simulations were performed with the SIMULINK/MATLAB and the results were compared with the experimental data reported on the manufacturer’s catalogue available in the literature. The main innovation introduced by the paper lies in the implicitly of the criteria proposed for the parameter identification of the dynamic model of arresters. Effectiveness and simplicity of use make the proposed method a useful tool for insulation coordination studies involving steep front transients.
Copyright © 2017 Praise Worthy Prize - All rights reserved.
H. J. Li, A parameter identification technique for metal-oxide surge arrester models, IEEE Transaction on Power Delivery, vol.17, n. 3, July 2002, pp736 – 741.
A. Bayadi, N. Harid, K. Zehar, S. Belkhiat, Simulation of metal oxide surge arrester dynamic behavior under fast transients, in Proceedings of the IPST’03, 2003.
F. Fernandez, Metal oxide surge arrester model for fast transient simulations, in Proceedings of the IPST’01, 2001.
IEEE Working Group 3.4.11, Modeling of metal oxide surge arresters, IEEE Transaction Power Delivery, vol. 7, n. 1, Jan. 1992, pp. 302–309.
A. R. Hileman, Insulation Coordination for Power Systems (Marcel Dekker, 1999), pp. 528–528.
P. Pinceti, M. Giannettoni, A simplified model for zinc oxide surge arresters, IEEE Transaction Power Delivery, vol. 14, n. 2, Apr. 1999, pp. 393–398.
I. Kim, T. Funabashi, T. Sasaki, T. Hagiwara, and M. Kobayashi, Study of ZnO arrester model for steep front wave, IEEE Transaction on Power Delivery, vol. 11, Apr. 1996, pp. 834–841.
A. Haddad, J. Fuentes-Rosado, D. M. German, and R. T.Walters, Characterization of ZnO surge arrester elements with direct and power frequency voltages, Proc. Inst. Elect. Eng., pt. A, vol. 137, Sept. 1990, pp. 267–279.
W. Schmidt, J Meppelink, B. Richter, K. Feser, L. Kehl and D. Qiu, Behavior of MO surge arrester blocks to fast transients, IEEE Transactions on Power Delivery, vol. 4, n. 1, Jan. 1989, pp. 292-300.
A. Haddad and P. Naylor, Dynamic response of ZnO arresters under high amplitude fast impulse currents, International power electric conference, 1999, pp. 292-297.
Surge Arresters Part 2: Metal Oxide Surge Arresters without Gaps for AC Systems. Australian Standard 1307.2. 1996.
S. Wyderka, Digital Model of Metal Oxide Surge ArresterBased on Catalog Data, ICLP Conference, pp. 778-783, 1998.
T. Funabashi, T. Hagiwara and H. Watanabe, Surge Arrester Model that Enables Highly Accurate Analysis of Lightning Surges, Meiden Review, No 118, pp.39 – 42, 2000.
M. C. Magro, M. Giannettoni, P. Pinceti, Validation of ZnO Surge Arresters Model for Overvoltage Studies, IEEE Transactions on Power Delivery, vol. 19, n. 4, Oct. 2004, pp. .
T. Saengsuwan, Lighting Arrester Modeling using ATP-EMTP, Bangkok, Kasetsart University, 2004.
E.C.Sakshaug, J. J. Bruke, J. S. Kresge, Metal Oxide Arrester on Distribution system Fundamental Considerations, IEEE Transaction on Power Delivery, vol.4, n. 4, Oct. 1989, pp 2076-2089.
A. Bayadi, Parameters Calculation of ZnO Surge Arrester Models by Genetic Algorithms, Journal of Electrical Systems, No. 2006.
Z. Michalewicz, Genetic algorithms + data structure = evolution programs, (Springer, 1999).
F. Alonge, Parameter identification of induction motor model using genetic algorithms, IEE Proc-Control Theory Appl., vol. 145, n. 6, 1998, pp.587-593.
D. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison-Wesley, 1989.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2019 Praise Worthy Prize