A Novel Technique for Ferroresonance Identification in Distribution Networks
Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Competitive Neural Network used for classification. Ferroresonance data and other transients was obtained by simulation using EMTP program. Using Daubechies wavelet transform signals has been decomposed till six levels. The energy of six detailed signals that obtained by wavelet transform are used for training and trailing Competitive Neural Network. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.
[1] Haili Ma and Adly Girgis, "Identification and tracking of Harmonics
Source in a Power System using Kalman filter" IEEE trans.on power
Delivery vol. 11, No. 3, July 1996.
[2] Irene Gu, math Bollen, "Time frequency and Timescale domain analysis
of voltage Disturbances" IEEE trans.on power Delivery vol. 15, No. 4,
October 2000.
[3] Surya Santoso, Jeff Lamoree, Mack Grady,Edward powers and
Siddharth.Bhatt." A Scalable PQ Event Identification System" IEEE
trans.on power Delivery vol. 15, No. 2, April 2000.
[4] A.Gaouda M.salama, M.Sultan and A.Chikhani, "Power Quality
Detection and Classification using Wavelet Multuresolution Signal
Decomposition", IEEE trans.on power Delivery vol.14,No.4, October 1999 Masoud karami, hossein Mokhtari and Reza Irvani ,"Wavelet Based On Line Disturbance Detection for Power Quality Application ", IEEE trans.on power Delivery vol.15,No.4, October 2000.
[5] J.Chung, E.J. Powers, W.M.G rady, and S.C.Bhatt," Electric Power
Transient Disturbance Classification Using wavelet Based Hidden
Markov Models", Proceeding of 2000 IEEE International Conference on
Acoustics, Speech and Signal Processing, vol.6, 2000.
[6] B. Perunicic, M. Mallini, Z. Wang, Y. Liu, and G. T. Heydt," Power
Quality Disturbance Detection and Classification Using Wavelets and
Artificial Neural Networks", The 8th International Conference on
Harmonics and Quality of Power,1998.
[7] C. L. Huang, H. Y. Chu and M. T. Chen, "Algorithm comparison for
High Impedance fault based on Staged Fault Test", IEEE trans.on power
Delivery vol.3, October 1988.
[8] H. Calhoun, M. T. Bishop, C.h.Eiceler and R. E. Lee, "Development and
Testing of an Electro-mechanical Relay to detect Fallen Distribution
Conductors", IEEE trans.on power apparatus and systems vol.PAS-
100(4), April 1998.
[9] Y. Sheng and S. M. Rovnyak, "Decision Tree- Based Methodology for
High Impedance Fault Detection", IEEE trans.on power Delivery vol.19,
April 2004.
[10] B. D. Russell, and R. P. Chinchali, "A Digital Signal processing
Algorithm for Detecting ASrcing Fault on Power Distribution Feeders ",
IEEE trans.on power Delivery, vol 4, January 1989.
[11] A. R. Sedighi, M. R. Haghifam, O. P. Malik, M. H. Ghassemian, "High
Impedance Fault Detection Based on Wavelet Transform and Statistical
pattern Recognition", IEEE trans. on power Delivery, 2005,pp. 2414 -
2421.
[12] D. C. Robertson, O. I. Camps, J. S.Mayer, and W. B. Gish,"Wavelets
and Electromagnetic Power System Transients", IEEE Trans. Power
Delivery, Vol. 11, pp.1050-1058, April-1996.
[13] P. L. Mao and R. K. Aggarwal, "A novel approach to the classification
of the transient phenomena in power transformers using combined
wavelet.
[14] Transform and neural network," IEEE Trans. Power Delivery, vol. 16,
pp. 654-660, Oct. 2001.
[15] S. G. Mallat, "A theory for multiresolution signal decomposition: the
wavelet representation," IEEE Trans. Pattern Anal. Machine Intel., vol.
11, pp. 674-693, Apr. 1989.
[16] Wavelet toolbox for Matlab, User manual, MathWorks, Natick, USA.
[1] Haili Ma and Adly Girgis, "Identification and tracking of Harmonics
Source in a Power System using Kalman filter" IEEE trans.on power
Delivery vol. 11, No. 3, July 1996.
[2] Irene Gu, math Bollen, "Time frequency and Timescale domain analysis
of voltage Disturbances" IEEE trans.on power Delivery vol. 15, No. 4,
October 2000.
[3] Surya Santoso, Jeff Lamoree, Mack Grady,Edward powers and
Siddharth.Bhatt." A Scalable PQ Event Identification System" IEEE
trans.on power Delivery vol. 15, No. 2, April 2000.
[4] A.Gaouda M.salama, M.Sultan and A.Chikhani, "Power Quality
Detection and Classification using Wavelet Multuresolution Signal
Decomposition", IEEE trans.on power Delivery vol.14,No.4, October 1999 Masoud karami, hossein Mokhtari and Reza Irvani ,"Wavelet Based On Line Disturbance Detection for Power Quality Application ", IEEE trans.on power Delivery vol.15,No.4, October 2000.
[5] J.Chung, E.J. Powers, W.M.G rady, and S.C.Bhatt," Electric Power
Transient Disturbance Classification Using wavelet Based Hidden
Markov Models", Proceeding of 2000 IEEE International Conference on
Acoustics, Speech and Signal Processing, vol.6, 2000.
[6] B. Perunicic, M. Mallini, Z. Wang, Y. Liu, and G. T. Heydt," Power
Quality Disturbance Detection and Classification Using Wavelets and
Artificial Neural Networks", The 8th International Conference on
Harmonics and Quality of Power,1998.
[7] C. L. Huang, H. Y. Chu and M. T. Chen, "Algorithm comparison for
High Impedance fault based on Staged Fault Test", IEEE trans.on power
Delivery vol.3, October 1988.
[8] H. Calhoun, M. T. Bishop, C.h.Eiceler and R. E. Lee, "Development and
Testing of an Electro-mechanical Relay to detect Fallen Distribution
Conductors", IEEE trans.on power apparatus and systems vol.PAS-
100(4), April 1998.
[9] Y. Sheng and S. M. Rovnyak, "Decision Tree- Based Methodology for
High Impedance Fault Detection", IEEE trans.on power Delivery vol.19,
April 2004.
[10] B. D. Russell, and R. P. Chinchali, "A Digital Signal processing
Algorithm for Detecting ASrcing Fault on Power Distribution Feeders ",
IEEE trans.on power Delivery, vol 4, January 1989.
[11] A. R. Sedighi, M. R. Haghifam, O. P. Malik, M. H. Ghassemian, "High
Impedance Fault Detection Based on Wavelet Transform and Statistical
pattern Recognition", IEEE trans. on power Delivery, 2005,pp. 2414 -
2421.
[12] D. C. Robertson, O. I. Camps, J. S.Mayer, and W. B. Gish,"Wavelets
and Electromagnetic Power System Transients", IEEE Trans. Power
Delivery, Vol. 11, pp.1050-1058, April-1996.
[13] P. L. Mao and R. K. Aggarwal, "A novel approach to the classification
of the transient phenomena in power transformers using combined
wavelet.
[14] Transform and neural network," IEEE Trans. Power Delivery, vol. 16,
pp. 654-660, Oct. 2001.
[15] S. G. Mallat, "A theory for multiresolution signal decomposition: the
wavelet representation," IEEE Trans. Pattern Anal. Machine Intel., vol.
11, pp. 674-693, Apr. 1989.
[16] Wavelet toolbox for Matlab, User manual, MathWorks, Natick, USA.
@article{"International Journal of Electrical, Electronic and Communication Sciences:52607", author = "G. Mokryani and M. R. Haghifam and J. Esmaeilpoor", title = "A Novel Technique for Ferroresonance Identification in Distribution Networks ", abstract = "Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Competitive Neural Network used for classification. Ferroresonance data and other transients was obtained by simulation using EMTP program. Using Daubechies wavelet transform signals has been decomposed till six levels. The energy of six detailed signals that obtained by wavelet transform are used for training and trailing Competitive Neural Network. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.
", keywords = "Competitive Neural Network, Ferroresonance,EMTP program, Wavelet transform.", volume = "1", number = "4", pages = "613-6", }
