Prediction of Voltage Dip Frequency in Turkish Energy Transmission Lines Based on Artificial Neural Networks

International Journal of Electrical and Electronics Engineering
© 2015 by SSRG - IJEEE Journal
Volume 2 Issue 2
Year of Publication : 2015
Authors : Mehlika Sengul, Elif Inan, Bora Alboyaci
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Citation:
MLA Style:

Mehlika Sengul, Elif Inan, Bora Alboyaci, "Prediction of Voltage Dip Frequency in Turkish Energy Transmission Lines Based on Artificial Neural Networks" SSRG International Journal of Electrical and Electronics Engineering 2.2 (2015): 6-12.

APA Style:

Mehlika Sengul, Elif Inan, Bora Alboyaci,(2019). Prediction of Voltage Dip Frequency in Turkish Energy Transmission Lines Based on Artificial Neural Networks. SSRG International Journal of Electrical and Electronics Engineering 2(2), 6-12.

Abstract:

Increasing usage of sensitive loads in industry due to rapid growth of power electronic equipment, give rise to focusing on power quality (PQ) phenomena. Voltage sag (voltage dip) is one of the most important PQ problems that utilities and customers are faced. Voltage dips may cause disoperation at sensitive loads of the power utility which could result not only loss of process but also economical damages, especially in paper, ironsteel and rubber industries. Therefore; elimination or reduction of this problem is significant. This paper presents a new approach for predicting the voltage dip frequency due to line fault of per km per month for rehabilitation of Turkish Transmission System. A neural network with feed forward structure has chosen as the prediction method and monitored data sets belonging to Turkish 380 kV voltage level transmission system have been used. It was seen that the approach can predict voltage dip frequency per km per month accurately.

References:

[1] C. Sankaran, “Power Quality”, pp:6-preface, pp:21-23- chapter:1, CRC Press New York 2002.
[2] R.C. Dugan, M. F. McGranaghan, S. Santoso, H.W. Beaty, “Electrical Power Systems Quality”, pp:14-chapter:2, pp:20-chapter:2, McGraw-Hill Companies, Inc., New York 2003.
[3] C. Benachaiba and B. Ferdi, “Power Quality ─░mprovement Using DVR”, American Journal of Applied Sciences, Vol.6, No:3, pp:396-400, 2009, doi: 10.3844/ajassp.2009.396.400.
[4] M. F. McGranaghan, D.R. Mueller and M.J. Samatyj, “Voltage Sags in Industrial Power Systems”, IEEE Trans. On Industry Applications, Vol.29, No:2, pp.379-403, 1993, doi:10.1109/28.216550.
[5] L.D.Zhang, M.H.J. Bollen, “A method for Characterizing Unbalanced Voltage Dips (sags) with Symmetrical Components”, IEEE Power Engineering Review, Vol.18, No:7, pp. 50-52, July 1998, doi:10.1109/39.691718.
[6] L. Zhang and M.H.J. Bollen, “Characteristic of Voltage Dips (Sags) in Power Systems”, IEEE Transactions on Power Delivery, Vol.15, No.2, pp.379-403, April 2000, doi:10.1109/61.853026.
[7] C. Radhakrishna, M. Eshwardas, M. Chebiyam, “Impact of Voltage Sags in Practical Power System Networks”, Transmission and Distribution Conference and Exposition, IEEE/PES, Vol.1, pp:567-572, 2001, doi:10.1109/TDC.2001.971296.
[8] G. Yalcinkaya, M.H..J. Bollen, P.A. Crossley, “Characterization of Voltage Sag in Industrial Distribution Systems”, IEEE Trans. On Industry Applications, Vol.34, No:4, pp.682-688, 1998, doi: 10.1109/28.703958.
[9] M.F. McGranaghan, D.R. Mueller, M.J. Samotej, “Voltage Sags in Industrial Power Systems”, IEEE Transactions on Industry Applications, vol. 29, no.2, pp.397-403, March 1993, doi:10.1109/28.216550.
[10] O. Samuelsson, M. Hemmingsson, A. H. Nielsen, K. O. H. Pedersen, and J. Rasmussen, “Monitoring of Power System Events at Transmission and Distribution Level”, IEEE Transactions on Power Systems,Vol.21, No.2, pp. 1007-1008, May 2006, doi: 10.1109/TPWRS.2006.873014.
[11] G. Gross, A. Bose, C. DeMarco, M. Pai, J. Thorp, and P. Varaiya, “Real Time Security Monitoring and Control of Power Systems”, The Consortium for Electric Reliability Technology Solutions Grid of the Future, Power Systems Engineering Research Center (PSERC), DE-AI- 99EE35075, Dec. 1999.
[12] F.J. Alcantare, J.R. Vazquez, P. Salmeron, A. Perez, “An ANN System to On-line Detection of Sag-Swell and Transient Voltages”, 11th.Spanish-Porteguese Conference on Electrical Engineering (11 CHLIE), Zaragoza 1-4 July 2009.
[13] P.R. Manke, S.B. Tembhurne, “Artificial Neural Network Classification of Power Quality Disturbances Using Time- Frequency Plane in Industries”, First International Conference on Emerging Trends in Engineering and Technology (ICETET) 2008, doi: 10.1109/ICETET.2008.53.
[14] M. Manjula, A.V.R.S. Sarma, “Classification of Voltage Sag Cause Using Probabilistic Neural Network and Hilbert-Huang Transform”, International Journal of Computer Applications, Vol.1, No.20, 2010, doi: 10.5120/427-630.
[15] M.R. Banaei, S.H. Hosseini, M.D. Khajee, “Mitigation of Voltage Sag Using Adaptive Neural Network with Dynamic Voltage Restorer”, Power Electronics and Motion Control Conference, 2006. IPEMC 2006, doi: 10.1109/IPEMC.2006.4778093.
[16] M.H.J. Bollen, T. Tayjasajant, G.Yalcinkaya, “Assessment of The Number of Voltage Sags Experienced by A Large Industrial Customer”, IEEE Transaction on Industry Applications, Nov/Dec 1997, doi: 10.1109/ICPS.1997.595980.
[17] M.T. Aung, J.V. Milanovic, “The Influence of Transformer Winding Connections on Propagation of Voltage Sags”, Power Delivery IEEE Transactions, vol. 21, pp. 262-269, 2006, doi: 10.1109/TPWRD.2005.855446.
[18] G. Olguin, “Voltage Dip (Sag) Estimation in Power Systems based on Stochastic Assessment and Optimal Monitoring”, PHd Thesis, Chalmers University of Technology, Sweden, 2005.
[19] TEIAS (Turkey Electric Transmission Incorporated Company), 2012.
[20] A. Bickel, “The Basic of Power Monitoring Systems”, Square D Co. / Schneider Electric, Electrical Construction and Maintenance, Dec 1, 2007.
[21] E. Özdemirci, Y. Akkaya, B. Boyrazo─člu, et.al. “Mobile Monitoring System to Take PQ Snapshots of Turkish Electricity Transmission System”, IMTC 2007., doi: 10.1109/IMTC.2007.379204.
[22] G. Diaz, P. Arboleya, J. Gomez-Aleixandre, “A New Transformer Differential Protection Approach on The Basis of Space-Vectors Examination”, Springer Verlag, Vol.87, No.3, April 2005, doi: 10.1007/s00202-004-0231- 9.
[23] IEC 61000-4-30, Electromagnetic Compatibility (EMC) – Part 4-30: Testing and Measurement Techniques – Power Quality Measurement Methods.

Key Words:

  Artificial neural networks, pattern recognition, power quality, transmission lines.