Optimized Power Quality Events Classifier

In this paper optimized classifier for classification of different power quality (PQ) events is presented. Its implementation is performed in LabVIEW, using an efficient-wavelet based feature extraction algorithm and an optimized random forest (RF) classification method. It is able to classify twenty-one classes of single and combined PQ events. Its accuracy is tested and verified using signals from three different sources, including real PQ events, with and without presence of white Gaussian noise. The verification has shown that this classifier exhibits high classification accuracy, despite the fact that in the process of classification two classes obtained as combination of four disturbances are considered.

[1]  Dimitar Taskovski,et al.  On the choice of wavelet based features in power quality disturbances classification , 2017, 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[2]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[3]  Guowei Cai,et al.  Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm , 2016 .

[4]  D. D. Ferreira,et al.  Real-time system for automatic classification of power quality disturbances , 2016, 2016 17th International Conference on Harmonics and Quality of Power (ICHQP).

[5]  Sukumar Mishra,et al.  Detection and classification of power quality disturbances using S-Transform and probabilistic neural network , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[6]  Huseyin Eristi,et al.  Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines , 2013 .

[7]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[8]  Francisco Arcega,et al.  Mathematical model of power quality disturbances , 2017 .

[9]  Dimitar Taskovski,et al.  Classification of power quality disturbances using wavelet transform and SVM decision tree , 2011, 11th International Conference on Electrical Power Quality and Utilisation.

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

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  D. Denic,et al.  Generation of Long-time Complex Signals for Testing the Instruments for Detection of Voltage Quality Disturbances , 2018 .

[13]  Sanjeeb Mohanty,et al.  Localization and Classification of Power Quality Disturbances using Maximal Overlap Discrete Wavelet Transform and Data Mining based Classifiers , 2016 .

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

[15]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[16]  M. Uyar,et al.  An effective wavelet-based feature extraction method for classification of power quality disturbance signals , 2008 .

[17]  E.F. El-Saadany,et al.  Power quality disturbance classification using the inductive inference approach , 2004, IEEE Transactions on Power Delivery.

[18]  Dimitar Taskovski,et al.  The effectiveness of wavelet based features on power quality disturbances classification in noisy environment , 2018, 2018 18th International Conference on Harmonics and Quality of Power (ICHQP).

[20]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[21]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

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

[23]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[24]  Meng-Hui Wang,et al.  A novel analytic method of power quality using extension genetic algorithm and wavelet transform , 2011, Expert Syst. Appl..

[25]  Cosmin Darab,et al.  Management of Power Quality Issues from an Economic Point of View , 2018, Sustainability.