Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Power Disturbance Classifier Using Wavelet-Based Neural Network

  • Choi Jae-Ho (School of Electrical and Computer Eng., Chungbuk National University) ;
  • Kim Hong-Kyun (School of Electrical and Computer Eng., Chungbuk National University) ;
  • Lee Jin-Mok (School of Electrical and Computer Eng., Chungbuk National University) ;
  • Chung Gyo-Bum (Department of Electrical, Hongik University)
  • Published : 2006.10.01
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Abstract

This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.

Keywords

References

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