Detection and classification of power quality disturbances with wavelet transform, decision tree algorithm and support vector machines

The main goal in this article is to present a comparative analysis of two well-known classification methods applied to the detection and classification of power quality disturbance signals. The Decision-Tree algorithm and Support Vector Machines are two of the most valuable and popular techniques used in the pattern recognition field. In this paper, power quality signals are generated through the Matlab/Simulink software and then classified in the trained model built. The method chosen for the characteristics extraction is the discrete Wavelet transform due to its great advantages of reducing the amount of data for computation and its perfect reconstruction. The analysis is performed for different kinds of Wavelet filters, noise levels and characteristic vectors.

[1]  Edward J. Powers,et al.  Electric power quality disturbance detection using wavelet transform analysis , 1994, Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis.

[2]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[3]  Irene Y. H. Gu,et al.  Emerging Signal Processing Techniques for Power Quality Applications , 2007, EURASIP J. Adv. Signal Process..

[4]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[5]  Math Bollen,et al.  Understanding Power Quality Problems , 1999 .

[6]  Xuejun Dong,et al.  Wavelets for Agriculture and Biology: A Tutorial with Applications and Outlook , 2008 .

[7]  N. Kagan,et al.  Selecting the best wavelet function for power quality disturbances identification patterns , 2008, 2008 13th International Conference on Harmonics and Quality of Power.

[8]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[9]  A. Poularikas.  The transforms and applications handbook , 2000 .

[10]  Zhang Ming,et al.  Power Quality Disturbance Data Compression Using JPEG2000 , 2011 .

[11]  Dianguo Xu,et al.  Power quality disturbance recognition using stransforms and FCM-based decision tree , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[12]  R. Sukanesh,et al.  Power quality disturbance classification using Hilbert transform and RBF networks , 2010, Neurocomputing.

[13]  Gabriel Ordonez,et al.  Automatic power quality disturbance classification using wavelet, Support Vector Machine and Artificial Neural Network , 2009 .

[14]  P. K. Dash,et al.  Power Quality Disturbance Data Compression, Detection, and Classification Using Integrated Spline Wavelet and S-Transform , 2002, IEEE Power Engineering Review.

[15]  R. Kalidoss,et al.  Introduction to Analog and Digital Communication , 2016 .

[16]  Juan José Dañobeitia,et al.  The $S$-Transform From a Wavelet Point of View , 2008, IEEE Transactions on Signal Processing.

[17]  Gabriel Ordonez,et al.  AUTOMATIC POWER QUALITY DISTURBANCES DETECTION AND CLASSIFICATION BASED ON DISCRETE WAVELET TRANSFORM AND SUPPORT VECTOR MACHINES , 2007 .

[18]  Irene Yu-Hua Gu,et al.  Signal processing of power quality disturbances , 2006 .

[19]  Amara Lynn Graps,et al.  An introduction to wavelets , 1995 .

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