Rough-Set-Based Feature Selection and Classification for Power Quality Sensing Device Employing Correlation Techniques

In this paper, we present a scheme of rough-set-based minimal set of feature selection and classification of power quality disturbances that can be implemented in a general-purpose microcontroller for embedded applications. The developed scheme can efficiently sense the power quality disturbances by the features extracted from the cross-correlogram of power quality disturbance waveforms. In this paper, a stand-alone module, employing microcontroller-based embedded system, is devised for efficiently sensing power quality disturbances in real time for in situ applications. The stand-alone module is developed on a PIC24F series microcontroller. Results show that the accuracy of the proposed scheme is comparable to that obtained in offline analysis using a computer. The method stated here is generic in nature and can be implemented for other microcontroller-based applications for topologically similar problems.

[1]  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.

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

[3]  Zdzislaw Pawlak,et al.  Rough Set Theory and its Applications to Data Analysis , 1998, Cybern. Syst..

[4]  Mohammad A. S. Masoum,et al.  Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks , 2010 .

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

[6]  M. Negnevitsky,et al.  A Neural-Fuzzy Classifier for Recognition of Power Quality Disturbances , 2002, IEEE Power Engineering Review.

[7]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[8]  Andrzej SkowronInstitute,et al.  QUANTIZATION OF REAL VALUE ATTRIBUTESRough Set and Boolean Reasoning , 1995 .

[9]  Debangshu Dey,et al.  Cross-correlation-aided Fuzzy c-Means for Classification of Dynamic Faults in Transformer Winding During Impulse Testing , 2010 .

[10]  R.R. Harrison,et al.  Microcontroller-Based Wireless Recording Unit for Neurodynamic Studies in Saltwater , 2006, IEEE Sensors Journal.

[11]  Amitava Chatterjee,et al.  Support vector machines employing cross-correlation for emotional speech recognition , 2009 .

[12]  Chen-Fu Chien,et al.  Rough set theory for data mining for fault diagnosis on distribution feeder , 2004 .

[13]  Amitava Chatterjee,et al.  Cross-correlation aided support vector machine classifier for classification of EEG signals , 2009, Expert Syst. Appl..

[14]  Andrew Kusiak,et al.  Rough set theory: a data mining tool for semiconductor manufacturing , 2001 .

[15]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[16]  Gautam Biswas,et al.  An approach for fault detection and isolation in dynamic systems from distributed measurements , 2002, IEEE Trans. Instrum. Meas..

[17]  Kitti Attakitmongcol,et al.  Wavelet-based neural network for power quality disturbance recognition and classification , 2006 .

[18]  B. Chatterjee,et al.  Rough-granular approach for impulse fault classification of transformers using cross-wavelet transform , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

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

[20]  José Edison Cabral,et al.  Fraud detection in electrical energy consumers using rough sets , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[21]  Paul W. Prickett,et al.  A Petri-net based distributed monitoring system using PIC microcontrollers , 2005, Microprocess. Microsystems.

[22]  Weiming Tong,et al.  Detection and Classification of Power Quality Disturbances Based on Wavelet Packet Decomposition and Support Vector Machines , 2006, 2006 8th international Conference on Signal Processing.

[23]  Gautam Sarkar,et al.  Development of a Microcontroller-Based Frequency Synthesizer cum Vector Voltmeter , 2011, IEEE Sensors Journal.

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