Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31824
Distance Transmission Line Protection Based on Radial Basis Function Neural Network

Authors: Anant Oonsivilai, Sanom Saichoomdee


To determine the presence and location of faults in a transmission by the adaptation of protective distance relay based on the measurement of fixed settings as line impedance is achieved by several different techniques. Moreover, a fast, accurate and robust technique for real-time purposes is required for the modern power systems. The appliance of radial basis function neural network in transmission line protection is demonstrated in this paper. The method applies the power system via voltage and current signals to learn the hidden relationship presented in the input patterns. It is experiential that the proposed technique is competent to identify the particular fault direction more speedily. System simulations studied show that the proposed approach is able to distinguish the direction of a fault on a transmission line swiftly and correctly, therefore suitable for the real-time purposes.

Keywords: radial basis function neural network, transmission lines protection, relaying, power system.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2197


[1] P.M. Anderson, Power system protection, McGraw-Hill, 1999.
[2] K. Warwick, A. Ekwue and R. Aggarwal, Artificial intelligence techniques in power System, London, Institution of Electrical Engineers, 1997
[3] H. B. Demuth, M. Beale, Neural Network Toolbox for Use with MATLAB, 1998.
[4] M. T. Hagan, H. B. Demuth, M. Beale, Neural Network Design, Oklahoma State University, 1996.
[5] W. Qi, G.W. Swift, P. G. McLaren, A. V. Castro, "An artificial neural network application to distance protection". International Conference on Intelligent Systems Applications to Power Systems, pp. 226-230,1996.
[6] D. V. Coury, D. C. Jorge, "Artificial neural network approach to distance protection of transmission lines". IEEE Transactions on Power Delivery, pp. 102-108, 1998.
[7] L. Wu, C. Liu, C. Chen, "Modeling and testing of a digital distance relay using MATLAB/SIMULINK". IEEE Trans. on Power Delivery, pp.253-259, 2005.
[8] A. Oonsivilai, and M.E El-Hawary, "Power system dynamic load modeling using adaptive-network-based fuzzy inference system Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, pp 1217-1222, 1999.
[9] A. Oonsivilai, and M.E. El-Hawary, "Wavelet neural network based short term load forecasting of electric power system commercial load". Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, pp. 1223-1228, 1999.
[10] A. Oonsivilai, and M. E. El-Hawary. "A self-organizing fuzzy power system stabilizer". Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, pp. 197-200. Canada, 1999.
[11] A. Oonsivilai, R. Boonwuitiwiwat, T. Kulworawanichpong, and P. Pao- La-Or, "Artificial neural network approach to electric field approximation around overhead power transmission lines". EuroPes 2007.
[12] R. Oonsivilai, and A. Oonsivilai, "Probabilistic neural network classification for Model β-Glucan Suspensions". Proceeding of the 7th WSEAS Int. Conf. on Simulation, Modeling and Optimization, pp. 159-164, 2007.
[13] C. L. Wadhwa, Electrical Power Systems, Fourth Edition, New Age International, 2006.
[14] A. G. Phadke and J. S. Thorp, Computer Relaying for Power Systems, John Wiley & Sons, Ltd. 1988.
[15] A. A. Girgis and R. G. Brown, "Adaptive Kalman Filtering Computer Relaying: Fault Classification Using Voltage Models", IEEE Transaction on Power Apparatus and System, Vol. PAS-104, No. 5, pp. 1167-1177, May 1985.
[16] S. Rajasekaran and G. A. Vijayalakshmi Pai, Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications, Prentice- Hal Ltd., 2003.
[17] L. H. Tsoukalas, and A. U. R. Uhrig, Fuzzy and neural in Engineering, John Wiley and Sons, Inc., 1997.
[18] A. Oonsivilai., and B. Marungsri, "Application of artificial intelligent technique for pratial discharges localization in oil insulating transformer". WSEAS Transaction on Systems.. Issue 10, Vol 8, October, ISSN : 1109 - 2777, pp: 920 -929.2008
[19] R. Oonsivilai, and A. Oonsivilai. "Apply a genetic algorithm to natural cheese product". Proceeding of the 8th WSEAS International conference on applied computer science (ACS-08). ISSN 1790 - 5109, pp: 269 - 274. 2008.
[20] A. Oonsivilai.,. and R. Oonsivilai, "A genetic algorithm application in natural cheese products". WSEAS Transaction on Systems. Issue 1, Vol 8, January, ISSN : 1109 - 2777, pp:44-54.,2009
[21] S. Saichoomdee, A. Oonsivilai ,B. Marungsri, T. Kulworawanichpong and P. Pao-La-Or. "Distance transmission lines protection based on recurrent neural network". International Conference on Science, Technology and Innovation for Sustainable Well-Being.(STISWB)., pp. 266-269. ,2009
[22] A. Oonsivilai and S. Saichoomdee "Appliance of Recurrent Neural Network toward Distance Transmission Lines Protection". IEEE TENCON '2009/Singapore ,2009