Power Quality Disturbances Recognition Based on a Multiresolution Generalized S-Transform and a PSO-Improved Decision Tree

In a microgrid, the distributed generators (DG) can power the user loads directly. As a result, power quality (PQ) events are more likely to affect the users. This paper proposes a Multiresolution Generalized S-transform (MGST) approach to improve the ability of analyzing and monitoring the power quality in a microgrid. Firstly, the time-frequency distribution characteristics of different types of disturbances are analyzed. Based on the characteristics, the frequency domain is segmented into three frequency areas. After that, the width factor of the window function in the S-transform is set in different frequency areas. MGST has different time-frequency resolution in each frequency area to satisfy the recognition requirements of different disturbances in each frequency area. Then, a rule-based decision tree classifier is designed. In addition, particle swarm optimization (PSO) is applied to extract the applicable features. Finally, the proposed method is compared with some others. The simulation experiments show that the new approach has better accuracy and noise immunity.

[1]  Wilsun Xu,et al.  Information extraction from PQ disturbances — An emerging direction of power quality research , 2012, 2012 IEEE 15th International Conference on Harmonics and Quality of Power.

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

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

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Thai Nguyen,et al.  International Journal of Emerging Electric Power Systems Power Quality Disturbance Classification Based on Adaptive Neuro-Fuzzy System Thai Nguyen , 2011 .

[6]  ChunXia Zhao,et al.  Particle swarm optimization with adaptive population size and its application , 2009, Appl. Soft Comput..

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  Guolong Chen,et al.  A New Strategy of Acceleration Coefficients for Particle Swarm Optimization , 2006, 2006 10th International Conference on Computer Supported Cooperative Work in Design.

[9]  José A. Aguado,et al.  Rule-based classification of power quality disturbances using S-transform , 2012 .

[10]  T. Lobos,et al.  Automated classification of power-quality disturbances using SVM and RBF networks , 2006, IEEE Transactions on Power Delivery.

[11]  N. R. Watson,et al.  Power Quality State Estimator for Smart Distribution Grids , 2013, IEEE Transactions on Power Systems.

[12]  Rajiv Kapoor,et al.  Classification of power quality events – A review , 2012 .

[13]  I. Wasiak,et al.  Energy storage application in low-voltage microgrids for energy management and power quality improvement , 2014 .

[14]  Timothy C. Green,et al.  High-Quality Power Generation Through Distributed Control of a Power Park Microgrid , 2006, IEEE Transactions on Industrial Electronics.

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

[16]  J. Kennedy,et al.  Neighborhood topologies in fully informed and best-of-neighborhood particle swarms , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  B. K. Panigrahi,et al.  Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm , 2009 .

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

[19]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[20]  Ali Enshaee,et al.  Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm , 2010 .

[21]  Angelo Baggini,et al.  Handbook of Power Quality , 2008 .

[22]  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..

[23]  Hui Jin.  Classification for Power Quality Short Duration Disturbances Based on Generalized S-transform , 2012 .

[24]  Rene de Jesus Romero-Troncoso,et al.  Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review , 2011 .

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

[26]  Dianguo Xu,et al.  Power quality disturbances classification based on S-transform and probabilistic neural network , 2012, Neurocomputing.

[27]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[28]  Birendra Biswal,et al.  Automatic Classification of Power Quality Events Using Balanced Neural Tree , 2014, IEEE Transactions on Industrial Electronics.