Power signal disturbance classification using wavelet based neural network | Zendy

S. Suja | Zendy; Jovitha Jerome | Zendy

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Power signal disturbance classification using wavelet based neural network

Author(s) -

S. Suja,

Jovitha Jerome

Publication year - 2007

Publication title -

serbian journal of electrical engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.133

H-Index - 5

eISSN - 2217-7183

pISSN - 1451-4869

DOI - 10.2298/sjee0701071s

Subject(s) - discrete wavelet transform , signal (programming language) , pattern recognition (psychology) , artificial neural network , wavelet , artificial intelligence , computer science , disturbance (geology) , power (physics) , wavelet transform , component (thermodynamics) , detector , engineering , telecommunications , paleontology , physics , quantum mechanics , biology , programming language , thermodynamics

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. These coefficients obtained from DWT are further subjected to statistical manipulations for increasing the detection accuracy. The third component is neural network architecture to classify the power signal disturbances with increased accuracy of detection

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