Power Quality Disturbances Recognition Based on HS-transform

The impact of power quality disturbance is one of the major factors of power quality. S-transform (ST) is a very effective method for power quality disturbances analysis which provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum. This paper proposed a new approach for power quality disturbances recognition using modified S-transform with hyperbolic Gaussian window known as HS-transform and automatic pattern classifier is carried out using rule-based decision tree. Various non-stationary power signals are processed by HS-transform to generate time-frequency features in order to generate rules for disturbances pattern classification. Compare to traditional S-transform, HS-transform is more precisely localized in the time domain. 6 types of disturbances are classified by the rule-based decision tree and there is no need to use other complicated classifiers. Simulation results show that the proposed method is feasible and promising for real applications.

[1]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. II. Application , 2000 .

[2]  Rengang Yang,et al.  Power-Quality Disturbance Recognition Using S-Transform , 2007, IEEE Transactions on Power Delivery.

[3]  N. Ertugrul,et al.  Automatic Classification and Characterization of Power Quality Events , 2008, IEEE Transactions on Power Delivery.

[4]  P. Dasha,et al.  Power quality monitoring using an integrated Fourier linear combiner and fuzzy expert system , 1999 .

[5]  G.T. Heydt,et al.  Power Quality as an Educational Opportunity , 2008, IEEE Transactions on Power Systems.

[6]  S. Mishra,et al.  Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network , 2008, IEEE Transactions on Power Delivery.

[7]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[8]  Bijaya K. Panigrahi,et al.  Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization , 2009, IEEE Transactions on Industrial Electronics.

[9]  Thai Nguyen,et al.  Power quality disturbance classification utilizing S-transform and binary feature matrix method , 2009 .

[10]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation , 2000 .

[11]  N. Ertugrul,et al.  Investigation of Effective Automatic Recognition Systems of Power-Quality Events , 2007, IEEE Transactions on Power Delivery.

[12]  C. Robert Pinnegar,et al.  The S-transform with windows of arbitrary and varying shape , 2003 .

[13]  Bijaya K. Panigrahi,et al.  Non-stationary power signal processing for pattern recognition using HS-transform , 2009, Appl. Soft Comput..

[14]  G. Panda,et al.  Power Quality Analysis Using S-Transform , 2002, IEEE Power Engineering Review.

[15]  Muhsin Tunay Gençoglu,et al.  An expert system based on S-transform and neural network for automatic classification of power quality disturbances , 2009, Expert Syst. Appl..

[16]  Magdy M. A. Salama,et al.  Power quality monitoring using an integrated Fourier linear combiner and fuzzy expert system , 1999 .

[17]  M.E. El-Hawary,et al.  Wavelet Packet Transform-Based Power Quality Indices for Balanced and Unbalanced Three-Phase Systems Under Stationary or Nonstationary Operating Conditions , 2009, IEEE Transactions on Power Delivery.

[18]  H. He,et al.  A self-organizing learning array system for power quality classification based on wavelet transform , 2006, IEEE Transactions on Power Delivery.