MATLAB/Simulink based modeling and simulation of power quality disturbances

The continuous monitoring of power quality (PQ) disturbances in electrical power distribution system has become an important issue for utilities and customers. The power system operation can be improved and maintained by analyzing the PQ disturbances systematically. In this paper, an attempt has been made to review the modeling and simulation of the PQ disturbances due to the exploitation of various types of loads. The PQ disturbances are created by using parametric equations as well as electrical power distribution system models in MATLAB/SIMULINK environment. The PQ disturbances of voltage magnitude variation such as sag, swell and interruption are created by applying different types of faults and heavy load in the power distribution model. The frequency variation types of PQ disturbances like harmonics are generated by applying power electronic converter. The non-stationary or transient PQ disturbances are produced by applying a capacitor switching bank in distribution model. The results of PQ disturbance waveforms obtained by both techniques are very similar to realtime PQ signals. The PQ waveforms obtained are suitable for checking the performance of the new automatic classification algorithms.

[1]  Pradipta Kishore Dash,et al.  Power quality event characterization using support vector machine and optimization using advanced immune algorithm , 2013, Neurocomputing.

[2]  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..

[3]  Yakup Demir,et al.  A new algorithm for automatic classification of power quality events based on wavelet transform and SVM , 2010, Expert Syst. Appl..

[4]  Z. Gaing.  Wavelet-based neural network for power disturbance recognition and classification , 2004 .

[5]  Hong-Tzer Yang,et al.  A de-noising scheme for enhancing wavelet-based power quality monitoring system , 2001 .

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

[7]  K. Manimala,et al.  Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining , 2011, Appl. Soft Comput..

[8]  Abdulhamit Subasi,et al.  Detection of generated and measured transient power quality events using Teager Energy Operator , 2011 .

[9]  Seyed Hossein Hosseinian,et al.  Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster–Shafer algorithm , 2013 .

[10]  K. Manimala,et al.  Optimization techniques for improving power quality data mining using wavelet packet based support vector machine , 2012, Neurocomputing.

[11]  Girish Kumar Singh,et al.  Statistical decision-tree based fault classification scheme for protection of power transmission lines , 2012 .

[12]  Math Bollen.  What is power quality , 2003 .

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

[14]  Qian Qing-quan,et al.  Study of a new method for power system transients classification based on wavelet entropy and neural network , 2011 .

[15]  Azah Mohamed,et al.  An image processing based method for power quality event identification , 2013 .

[16]  Arturo Garcia-Perez,et al.  Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks , 2014, IEEE Transactions on Industrial Electronics.