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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29737


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1434
Artificial Intelligence Techniques Applications for Power Disturbances Classification
Abstract:
Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.
Digital Object Identifier (DOI):

References:

[1] Mishra S, C. Bhende N, Panigrahi B. K., "Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network", IEEE Transactions On Power Delivery, January 2008, Vol. 23,pp.280-287.
[2] Gaing Zwe-Lee, "Wavelet-Based Neural Network for Power Disturbance Recognition and Classification", IEEE transactions on Power delivery,October 2004,vol. 19, pp1560-1567.
[3] Janik Przemyslaw and Lobos Tadeusz, "Automated Classification of Power-Quality Disturbances Using SVM and RBF Networks", IEEE Transactions On Power Delivery, July 2006,Vol. 21, Pp.1663-1669.
[4] S. Chen and H. Y. Zhu, "Wavelet Transform for Processing Power Quality Disturbances", EURASIP Journal on Advances in Signal Processing Volume 2007, Article ID 47695, 20 pages.
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[7] Z.Chen,Senior Member , IEEE, and P.Urwin "Power Quality Detection and Classification Using Digital Filters" IEEE Porto Power Tech Conference 2001.
[8] Peter G. V. Axelberg, Irene Yu-Hua Gu, Senior Member,,"Support Vector Machine for Classification of Voltage Disturbances", IEEE, and Math H. J. Bollen, Fellow, IEEE, IEEE Transactions On Power Delivery, Vol. 22, No. 3, July 2007
[9] Chih-Wei Hsu and Chih-Jen Lin, A Comparison of Methods for Multiclass Support Vector Machines, IEEE Transactions On Neural Networks, Vol. 13, NO. 2, March 2002 pp.415-425.
[10] Khalid Benabdeslem and Youn`es Bennani, "Dendogram- based SVM for Multi-Class Classification", Journal of Computing and Information Technology - CIT 14, 2006, 283-289.
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