Distance Transmission Line Protection Based on Radial Basis Function Neural Network
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.
[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
[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
@article{"International Journal of Electrical, Electronic and Communication Sciences:58587", author = "Anant Oonsivilai and Sanom Saichoomdee", title = "Distance Transmission Line Protection Based on Radial Basis Function Neural Network", abstract = "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.", volume = "3", number = "12", pages = "2356-4", }
