Abstract
Artificial neural networks (ANNs) are addressed in order to estimate the electric field across medium voltage surge arresters, information which is very useful for diagnostic tests and design procedures. Actual input and output data collected from hundreds of measurements carried out in the High Voltage Laboratory of the National Technical University of Athens (NTUA) are used in the training, validation and testing process. The developed ANN method can be used by laboratories and manufacturing/retail companies dealing with medium voltage surge arresters which either face a lack of suitable measuring equipment or want to compare/verify their own measurements.
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References
Aggarwal, R., Song, Y.: Artificial neural networks in power systems. III Examples of applications in power systems. Power Engin. Journal 12(6), 279–287 (1998)
Mahanty, R.N., Gupta, P.B.D.: Application of RBF neural network to fault classification and location in transmission lines. IEE Proc-Gen Tran. Distr. 151(2), 201–212 (2004)
Mazon, A.J., Zamora, I., Gracia, J., Sagastabeutia, K.J., Saenz, J.R.: Selecting ANN structures to find transmission faults. IEEE Computer Appl. in Power 14(3), 44–48 (2001)
Vasilic, S., Kezunovic, M.: An improved neural network algorithm for classifying the transmission line faults. Power Engin. Society Winter Meeting 2, 918–923 (2001)
Gardoso, G., Rolim, J.G., Zurn, H.H.: Application of neural-network modules to electric power system fault section estimation. IEEE Trans. on PWRD 19(3), 1034–1041 (2004)
Schmidt, H.P.: Application of artificial neural networks to the dynamic analysis of the voltage stability problem. IEE Proc-Gen Tran. Distr. 144(6), 371–376 (1997)
Paucar, V.L., Rider, M.J.: Artificial neural networks for solving the power flow problem in electric power systems. Electric Power Systems Research 62, 139–144 (2002)
Dash, P.K., Pradhan, A.K., Panda, G.: Application of minimal radial basis function neural network to distance protection. IEEE Trans. on PWRD. 16(1), 68–74 (2001)
Coury, D.V., Jorge, D.C.: Artificial neural network approach to distance protection of transmission lines. IEEE Trans. on PWRD 13(1), 102–108 (1998)
Cline, P., Lannes, W., Richards, G.: Use of pollution monitors with a neural network to predict insulator flashover. Electric Power Systems Research 42, 27–33 (1997)
Ahmad, A.S., Ghosh, P.S., Aljunid, S.A.K., Said, H.A.I., Hussain, H.: Artificial neural network for contamination severity assessment of high voltage insulators under various meteorological conditions. In: AUPEC, Perth (2001)
Miti, G.K., Moses, A.J.: Neural network-based software tool for predicting magnetic performance of strip-wound magnetic cores at medium to high frequency. IEE Proc-Sci. Meas. Technol. 151(3), 181–187 (2004)
Martinez, J.A., Gonzalez-Molina, F.: Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks. Power Engin. Society Winter Meeting 3, 1133–1138 (2001)
Sidhu, T.S., Singh, H., Sachdev, M.S.: Design, implementation and testing of an artificial neural network based fault direction discrimination for protecting transmission lines. IEEE Trans. on PWRD 10(2), 697–706 (1995)
Hinrichsen, V.: Metal-oxide surge arresters, 1st edn. Siemens (2001)
James, R.E., Su, Q.: Condition assessment of high voltage insulation in power system equipment, 1st edn. IET Power and Energy Series, p. 53 (2008)
Vahidi, B., Nasab, R.S., Moghani, J., Sh., K.S.A., Hosseinian, S.H.: Three dimensional analyses of electric field and voltage distribution on ZnO surge arrester with broken sheds. In: 2005 IEEE/PES Trans. and Distrib. Conf. & Exhib.: Asia and Pacific, Dalian, China (2005)
Meshkatoddini, M.R.: Study of the electric field intensity in bushing integrated ZnO surge arresters by means of finite element analysis. In: COSMOL Users Conf., Boston (2006)
Lundquist, J., Stenstrom, L., Schei, A., Hansen, B.: New method of the resistive leakage currents of metal-oxide surge arresters in service. IEEE Trans. on PWRD 5(4), 1811–1822 (1990)
Vahidi, B., Nasab, R.S., Moghani, J.S.: Analysis of electric field and voltage distributions on ZnO surge arrester for polluted condition. In: XIV Int. Symp. on High Voltage Engin., Tsinghua University, Beijing, China (2005)
Karthik, R.: A novel analysis of voltage distribution in zinc oxide arrester using finite element method. Int. J. of Recent Trends in Engineering 1(4), 1–3 (2009)
Han, S.J., Zou, J., Gu, S.Q., He, J.L., Yuan, J.S.: Calculation of the potential distribution of high voltage metal oxide arrester by using an improved semi-analytic finite element method. IEEE Trans. on Magnetics 41(5), 1392–1395 (2005)
Abe, S.: Neural networks and fuzzy systems. Kluwer Academic Publishers, Boston (1997)
Haykin, S.: Neural Networks: a comprehensive foundation. MacMillan College Publishing Company, New York (1994)
Maghami, P.G., Sparks, D.W.: Design of neural networks for fast convergence and accuracy: dynamics and control. IEEE Trans. on Neural Networks 11(1), 113–123 (2000)
Nolles, O.: Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Berlin (2001)
Lippmann, R.: An introduction to computing with neural nets. IEEE ASSP Magazine 4(2), 4–22 (1987)
Tamura, S.I., Tateishi, M.: Capabilities of a four-layered feedforward neural network: four layers versus three. IEEE Trans. on Neural Nets 8(2), 251–255 (1997)
Demuth, H., Beale, M.: Neural network toolbox user’s guide for use with MATLAB (2002)
Hagan, M.T., Demuth, H.P., Beale, M.: Neural network design. PWS Publishing, Boston (1996)
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Ekonomou, L., Christodoulou, C.A., Mladenov, V. (2014). Estimation of the Electric Field across Medium Voltage Surge Arresters Using Artificial Neural Networks. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_22
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DOI: https://doi.org/10.1007/978-3-319-11071-4_22
Publisher Name: Springer, Cham
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