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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Biswal, B. Biswal, M. Rao, M.V.N. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of ECE, SILICON Institute of Technology, Bhubaneswar, Orissa, India (Biswal, M.) || Department of ECE, GMR Institute of Technology, Rajam, A. P, India (Biswal, B.; Rao, M.V.N.) |
| Abstract | This paper presents a new approach for the classification of non-stationary signal patterns in an electric power network using a modified wavelet transform and neural network. The wavelet transform is phase corrected to yield a new transform known as S-transform, which has an excellent time-frequency resolution characteristic. The phase correction absolutely references the phase of the wavelet transform to the zero time point, thus assuring that the amplitude peaks are regions of stationary phase. Once the features of noisy time varying signal during steady state or transient condition are extracted using the S-transform, they are passed through either a feedforward neural network or a probabilistic neural network for pattern classification. The average classification accuracy of the noisy signals due to disturbances in the power network is of the order 95%. This document gives formatting instructions for authors preparing papers for publication in the Proceedings of an IEEE conference. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 536043 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424490721 |
| e-ISBN | 9781424490745 |
| DOI | 10.1109/INDCON.2010.5712619 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-17 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wavelet transforms PNN Accuracy Noise Artificial neural networks Non-stationary signal Feedforward neural networks Transient analysis Power Quality S-transform |
| Content Type | Text |
| Resource Type | Article |
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