Identification of ferroresonance based on wavelet transform and artificial neural networks

A novel method for Ferroresonance detection is presented in this paper. Using this method Ferroresonance can he 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 are obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.

[1]  Edward J. Powers,et al.  A scalable PQ event identification system , 2000 .

[2]  A. S. Morched,et al.  Modeling and analysis guidelines for slow transients. III. The study of ferroresonance , 2000 .

[3]  Robert Lee,et al.  Development and Testing of an Electro-Mechanical Relay to Detect Fallen Distribution Conductors , 1982, IEEE Transactions on Power Apparatus and Systems.

[4]  J. S. Mayer,et al.  Wavelets and electromagnetic power system transients , 1996 .

[5]  A. Y. Chikhani,et al.  Power quality detection and classification using wavelet-multiresolution signal decomposition , 1999 .

[6]  B. Perunicic,et al.  Power quality disturbance detection and classification using wavelets and artificial neural networks , 1998, 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.98EX227).

[7]  Edward J. Powers,et al.  Electric power transient disturbance classification using wavelet-based hidden Markov models , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[8]  Adly A. Girgis,et al.  Power system transient and harmonic studies using wavelet transform , 1999 .

[9]  S.M. Rovnyak,et al.  Decision tree-based methodology for high impedance fault detection , 2004, IEEE Transactions on Power Delivery.

[10]  O.P. Malik,et al.  High impedance fault detection based on wavelet transform and statistical pattern recognition , 2005, IEEE Transactions on Power Delivery.

[11]  Jaideva C. Goswami,et al.  Fundamentals of wavelets , 1999 .

[12]  B. D. Russell,et al.  A digital signal processing algorithm for detecting arcing faults on power distribution feeders , 1989 .

[13]  S. Santoso,et al.  Power quality assessment via wavelet transform analysis , 1996 .

[14]  M. R. Iravani,et al.  Wavelet based on-line disturbance detection for power quality applications , 2000 .

[15]  Math Bollen,et al.  Time-frequency and time-scale domain analysis of voltage disturbances , 2000 .

[16]  S. Mallat.  A wavelet tour of signal processing , 1998 .