LOCATION OF VOLTAGE SAG SOURCE BY USING ARTIFICIAL NEURAL NETWORK
DOI:
https://doi.org/10.11113/jt.v79.8447Keywords:
Power quality, voltage sag, multi-monitor based method, radial basis function network, voltage deviation indexAbstract
Power quality (PQ) is a major concern for number of electrical equipment such as sophisticated electronics equipment, high efficiency variable speed drive (VSD) and power electronic controller. The most common power quality event is the voltage sag. The objective is to estimate the location of voltage sag source using ANN. In this paper, the multi-monitor based method is used. Based on the simulation results, the voltage deviation (VD) index of voltage sag is calculated and assigned as a training data for ANN. The Radial Basis Function Network (RBFN) is used due to its superior performances (lower training time and errors). The three types of performance analysis considered are coefficient of determination (R2), root mean square error (RMSE) and sum of square error (SSE). The RBFN is developed by using MATLAB software. The proposed method is tested on the CIVANLAR distribution test system and the Permas Jaya distribution network. The voltage sags are simulated using Power World software which is a common simulation tool for power system analysis. The asymmetrical fault namely line to ground (LG) fault, double line to ground (LLG) fault and line to line (LL) fault are applied in the simulation. Based on the simulation results of voltage sag analysis, the highest VD is contributed by LLG for both test systems. Based on the proposed RBFN results, the best performance analysis are R2, RMSE and SSE of 0.9999, 5.24E-04 and 3.90E-05, respectively. Based on the results, the highest VD shows the location of voltage sag source in that system. The proposed RBFN accurately identifies the location of voltage sag source for both test systems.
References
M. H. J. Bollen and E. Styvaktakis. 2000. Tutorial on Voltage Sag Analysis. Ninth International Conference on Harmonics and Quality of Power Proceedings Volume III. Orlando, Florida, USA. 1-4 Oct 2000.
B. W. Kennedy. 2000. Power Quality Primer. United State of America. McGraw-Hill
S. M. Deshmukh, B. Dewani and S. P. Gawande. 2013. A Review of Power Quality Problems-Voltage Sags for Different Fault. International Journal of Scientific Engineering and Technology. 2(5): 392-397.
IEEE Standards Board. 1995. IEEE Std. 1159-1995. IEEE Recommended Practice for Monitoring Electric Power Quality. IEEE Inc. New York.
A. Kazemi, A. Mohamed, H. Shareef and H. Zayandehroodi. 2013. Review of Voltage Sag Source Identification Methods for Power Quality Diagnosis. PRZEGLÄ„D ELEKTROTECHNICZNY. 89(8): 143-149.
C. Sankaran. 2002. Power Quality. United State of America. CRC Press LLC.
J. A. Martinez and J. M. Arnedo. 2006. Voltage Sag Studies in Distribution Network-Part III: Voltage Sag Index Calculation. IEEE Transactions on Power Delivery. 21(3): 1689-1697.
J. A. Martinez and J. M. Arnedo. 2006. Voltage Sag Studies in Distribution Network-Part II: Voltage Sag Assessment. IEEE Transactions on Power Delivery. 21(3): 1679-1688.
M. T. Haque and A. M. Kashtiban. 2007. Application of Neural Networks in Power Systems: A Review. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering. 1(6): 889-893.
L. J. Awalin, H. Mokhlis and A. H. Abu Bakar. 2012. Recent Developments in Fault Location Methods for Distribution Networks. PRZEGLÄ„D ELEKTROTECHNICZNY. 88(12): 206-212.
M. H. Moradi and Y. Mohammadi. 2012. Voltage Sag Source Location: A Review With Introduction Of A New Method. International Journal of Electrical Power and Energy Systems. 43(1): 29-39.
A. C. Parsons, W. M. Grady, E. J. Powers and J. C. Soward. 2000. A Direction Finder for Power Quality Disturbances Based Upon Disturbance Power and Energy. IEEE Transactions on Power Delivery. 15(3): 1081-1086.
N. Hamzah, A. Mohamed and A. Hussain. 2004. A New Approach to Locate the Voltage Sag Source Using Real Current Component. Electric Power Systems Research. 72: 113-123.
R. C. Leborgne. 2007. Voltage Sag Source Location at Grid Interconnections: A Case Study in the Zambian System. IEEE Power Tech. Lausanne, Switzerland. 1-5 July 2007. 1852-1858.
R.Ch. Leborgne and D. Karlsson. 2008. Voltage Sag Source Location Based on Voltage Measurements Only. Journal of Electrical Power Quality and Utilisation. 104(1): 25-30.
D. J. Sunil Dhas, T. R. Deva Prakash and P. Jenopaulc. 2012. Voltage Sag Source Location Using Artificial Neural Network. International Journal of Current Engineering and Technology. 2(1): 206-210.
A. Deihimi and A. Momeni. 2012. Neural Estimation of Voltage-sag Waveforms of Non-Monitored Sensitive Loads at Monitored Locations in Distribution Networks Considering DGs. Electric Power Systems Research. 92: 123-137.
M. Jamil, A. Kalam, A. Q. Ansari and M. Rizwan. 2014. Generalized Neural Network and Wavelet Transform Based Approach for Fault Location Estimation of a Transmission Line. Journal of Applied Soft Computing. 19: 322-332.
Lei Ye, Dahai You, Xianggen Yin, Ke Wang and Junchun Wu. 2014. An Improved Fault-Location Method For Distribution System Using Wavelets And Support Vector Regression. International Journal of Electrical Power and Energy Systems. 55: 467-472.
H. Shareef and A. Mohamed. 2013. An Alternative Voltage Sag Source Identification Method Utilizing Radial Basis Function Network. 22nd International Conference on Electricity Distribution. 10-13 June, Stockholm, Sweden.
A. Kazemi, A. Mohamed, H. Shareef and H. Zayandehroodi. 2014. Accurate Voltage Sag-source Location Technique for Power Systems Using GACp and Multivariable Regression Methods. International Journal of Electrical Power and Energy Systems. 56: 97-109.
S. Chen, C. F. N. Cowan and P. M. Grant. 1991. Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks. 2(2): 302-309.
S. Nojavan, M. Jalali and K. Zare. 2014. Optimal Allocation of Capacitors in Radial/Mesh Distribution Systems Using Mixed Integer Nonlinear Programming Approach. Electric Power Systems Research. 107: 119-124.
H. Demuth, M. Beale and M. Hagan. 2009. Neural Network Toolbox 6 User's Guide. United State of America. The MathWorks, Inc.
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