Wavelet-Based Intelligent System for Recognition of Power Quality Disturbance Signals

  • Suriya Kaewarsa
  • Kitti Attakitmongcol
  • Wichai Krongkitsiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


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.


Discrete Wavelet Transform Power Quality Harmonic Distortion Wavelet Domain Learning Vector Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Suriya Kaewarsa
    • 1
  • Kitti Attakitmongcol
    • 1
  • Wichai Krongkitsiri
    • 2
  1. 1.School of Electrical EngineeringSuranaree University of TechnologyNakhon RatchasimaThailand
  2. 2.School of Electrical EngineeringRajamangala University of Technology IsanSakon NakhonThailand

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