Wavelet-Based intelligent system for recognition of power quality disturbance signals

  • Authors:

  • Suriya Kaewarsa;Kitti Attakitmongcol;Wichai Krongkitsiri

  • Affiliations:

  • School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand;School of Electrical Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand;School of Electrical Engineering, Rajamangala University of Technology Isan, Sakon Nakhon, Thailand

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a new approach for the recognition of power quality disturbances using wavelet transform and neural networks. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency.