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S. Suja
Coimbatore Institute of Technology. Coimbatore - 14, TN
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Mathematics

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POWER SIGNAL DISTURBANCE CLASSIFICATION USING WAVELET BASED NEURAL NETWORK S. Suja; Jovitha Jerome
ASEAN Journal on Science and Technology for Development Vol. 25 No. 2 (2008): ASEAN Journal on Science and Technology for Development (AJSTD)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (258.07 KB) | DOI: 10.29037/ajstd.243

Abstract

In this paper, the power signal disturbances are detected using discrete wavelet transform (DWT) and categorized using neural networks. This paper presents a prototype of power quality disturbance recognition system. The prototype contains three main components. First a simulator is used to generate power signal disturbances. The second component is a detector which uses the technique of DWT to detect the power signal disturbances. DWT is used to extract disturbance features in the power signal. The third component is neural network architecture to classify the power signal disturbances.    
Co-Authors Jovitha Jerome