AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

Authors

  • Suhail Khokhar Quaid-e-Awam University of Engineering, Science & Technology Nawabshah Pakistan http://orcid.org/0000-0002-1095-9093
  • A. A. Mohd Zin Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • M. A. Bhayo Quaid-e-Awam University of Engineering, Science & Technology Nawabshah Paksitan
  • A. S. Mokhtar Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v79.5693

Keywords:

Power quality disturbances, wavelet transform, support vector machine

Abstract

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

Author Biographies

  • Suhail Khokhar, Quaid-e-Awam University of Engineering, Science & Technology Nawabshah Pakistan
    Studied Bachelor and Master in Electrical Engineering from QUEST Nawabshah in 2006 and 2012 respectively. Worked as lecturer and Assistant Professor since July 2007 to July 2012 and July 2012 to date in QUEST Nawabshah. Pursuing PhD in Electrical Engineering in UTM since September 2012.
  • A. A. Mohd Zin, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
    Abdullah Asuhaimi Mohd Zin  received the B.Sc. degree from Gadjah Mada University, Indonesia in 1976, the M.Sc. degree from University of Strathclyde, Strathclyde, U.K. in 1981, and the Ph.D. degree from the University of Manchester Institute of Science and Technology, Manchester, U.K., in 1988. Currently, he is a Professor at the Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru. His research interests include power system protection, application of neural network in power system, arcing fault in underground cables, power quality and dynamic equivalent of power systems. Dr. Mohd Zin is a corporate member of the Institution of Engineers, Malaysia (IEM) and a member of the Institute of Electrical Engineers (U.K.). He is a registered Professional Engineer (P. Eng.) in Malaysia and Chartered Engineer (C.Eng.) in the United Kingdom.
  • A. S. Mokhtar, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

    Dr. Ahmad Safawi Mokhtar received the B.E.E degree in 1986 from Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia. He completed a M.Sc degree in Electrical Power Engineering from University of Strathclyde, Glasgow, United Kingdom and the PhD degree in Electrical Engineering from The University of Manchester, Manchester, United Kingdom in 1990 and 2005, respectively. Presently, he is a Senior Lecturer at the Universiti Teknologi Malaysia, Johor Bahru, Malaysia. His research interest includes power quality, power system analysis dan fault location in power system.

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Published

2016-12-29

Issue

Vol. 79 No. 1: January 2017

Section

Science and Engineering

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How to Cite

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE. (2016). Jurnal Teknologi, 79(1). https://doi.org/10.11113/jt.v79.5693