M. Moghimi Haji, B. Vahidi*, S. H. Hosseinian

Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran, *vahidi@aut.ac.ir.

ABSTRACT

This paper presents a novel current transformer (CT) saturation detection approach based on Gaussian Mixture Models (GMMs). High accuracy is the advantage of this method. GMMs are trained with secondary current of CT. The appropriate performance of the proposed method is tested by simulation of different fault conditions in PSCAD/EMTDC software. The results show that the trained GMMs can successfully detect CT saturation with high accuracy.

Keywords: CT saturation, GMM, protective relaying, transient analysis.

DESCARGAR ARTÍCULO EN FORMATO PDF

References

[1] W. Rebizant and D. Bejmert, "Current-Transformer Saturation Detection With Genetically Optimized Neural Networks," IEEE Trans. Power Delivery, vol. 22, no. 2, pp. 820-827, 2007.         [ Links ]

[2] A. G. Phadke and J.S. Thorp, Computer Relaying for Power Systems: Research Studies Press Ltd., 1988, pp. 185-186.         [ Links ]

[3] Y. C. Kang et al., "An algorithm for compensating the secondary current of current transformers," IEEE Trans. Power Delivery, vol. 12, pp. 116-124, 1997.         [ Links ]

[4] T. Bunyagul et al., "Overcurrent protection using signals derived from saturated measurement CTs," in Proc. IEEE Power Eng. Soc. Summer Meeting, Vancouver, BC, Canada, 2001.         [ Links ]

[5] C. Fernandez, "An impedance-based CT saturation detection algorithm for bus-bar differential protection," IEEE Trans. Power Delivery, vol. 16, pp. 468-472, 2001.         [ Links ]

[6] Y.C. Kang et al., "A CT saturation detection algorithm," IEEE Trans. Power Delivery, vol. 19, no. 1, pp. 78-85, 2004.         [ Links ]

[7] N. Villamagna and P. A. Crossley, "A CT saturation algorithm using symmetrical components for current differential protection," IEEE Trans. Power Delivery, vol. 21, no. 1, pp. 38-45, 2006.         [ Links ]

[8] M. M. Saha et al., "Application of ANN methods for instrument transformer correction in transmission line protection," in Proc. 7th Int. Conf. Developments in Power System Protection, Amsterdam, The Netherlands, 2001, pp. 303-306.         [ Links ]

[9] D. Neibur, "Artificial neural networks for power systems," Report by Task Force 38.06.06, Electra vol. 159, 1995.         [ Links ]

[10] B. Xiang and T. Berger, "Efficient Text-Independent Speaker Verification with Structural Gaussian Mixture Models and Neural Network," IEEE Trans. Speech and Audio Processing., vol. 11, pp. 447-456, 2003.         [ Links ]

[11] Y. Huang et al., "A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses," IEEE Trans. Biomedical Engineering., vol. 52, pp. 1801-1811, 2005.         [ Links ]

[12] X. Zhou and X. Wang, "Optimization of Gaussian mixture model for satellite image classification," IEE Proc.-Vis. Image Signal Process, vol. 153, pp. 349-356, 2006.         [ Links ]

[13] M. Hamouz et al., "Feature-based affine-invariant detection and localization of faces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, pp. 1490-1495, 2005.         [ Links ]

[14] J.C. Principe and M. Motter, "Identification and Control of Aircrafts using Multiple Models and Adaptive Critics," NASA Proposal No. 04-0617, 2007.         [ Links ]

[15] A. Ruano, "Intelligent Control Systems Using Computational Intelligence Techniques," IEE Control Series, 2005.         [ Links ]

[16] T. Marwala et al., "Hidden Markov Models and Gaussian Mixture Models for Bearing Fault Detection Using Fractals," International Joint Conference on Neural Networks, 2006, pp. 3237-3242.         [ Links ]

[17] S. Jazebi et al., "Magnetizing Inrush Current Identification Using Wavelet Based Gaussian Mixture Models," Simulation Modeling Practice and Theory, vol. 17, pp. 991-1010, 2009.         [ Links ]

[18] S. Jazebi et al., "Time domain single-phase reclosure scheme for transmission lines based on dual-Gaussian mixture models," Engineering Applications of Artificial Intelligence, In Press, 2012.         [ Links ]

[19] G. Xiong et al., "Dynamical Gaussian mixture model for tracking elliptical living objects," Pattern Recognition Letters 27, pp. 838-842, 2006.         [ Links ]

[20] P. Paalanen et al., "Feature representation and discrimination based on Gaussian mixture model probability densities-Practices and algorithms," Pattern Recognition., vol. 39, pp. 1346-1358, 2006.         [ Links ]

[21] D .C. Jiles and D L. Atherton, "Theory of ferromagnetic hysteresis," Journal of Magnetism and Magnetic Materials, vol. 61, pp. 48-60, 1986.         [ Links ]

[22] PSCAD/EMTDC User's Manual, Manitoba HVDC Research Center, Winnipeg, MB, Canada, 1988.         [ Links ]

[23] U.D. Annakkage et al., "A Current transformer model based on the Jiles-Atherton theory of ferromagnetic hysteresis," IEEE Trans. Power Del., vol. 15, no. 1, pp. 57-61, 2000.         [ Links ]

[24] R. A. Naghizadeh et al., "Modeling of Inrush Current in Transformers Using Inverse Jiles-Atherton Hysteresis Model with a Neuro-Shuffled Frog-Leaping Algorithm Approach," accepted to IET Electric Power Application.         [ Links ]

[25] H. Khorashadi-Zadeh and M. Sanaye-Pasand, "Correction of Saturated Current Transformers Secondary Current Using ANNs", IEEE Trans. Power Delivery, vol. 21, no. 1, pp. 73-79, 2006.         [ Links ]

[26] R. A. Naghizadeh et al., "Parameter Identification of Jiles-Atherton Model using SFLA," International Journal for Computation and Mathematics in Electrical and Electronic Engineering (COMPEL), vol. 31, no. 4, pp. 1293-1309, 2012.         [ Links ]

[27] Y. Kang et al., "An algorithm for detecting CT saturation using the secondary current third-derivative function," in Proc. IEEE Bologna PowerTech Conf., 2003, pp. 320-326.         [ Links ]