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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mishra, S. |
| Copyright Year | 2009 |
| Description | Author affiliation: Indian Institute of Technology, India (Mishra, S.) |
| Abstract | This paper presents an S-Transform based probabilistic neural network (PNN) classifier for recognition of power quality (PQ) disturbances. The proposed method requires less number of features as compared to wavelet based approach for the identification of PQ events. The features extracted through the S-Transform are trained by a PNN for automatic classification of the PQ events. Since the proposed methodology can reduce the features of the disturbance signal to a great extent without losing its original property, less memory space and learning PNN time are required for classification. Eleven types of disturbances are considered for the classification problem. The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events. The classification performance of PNN is compared with a feedforward multilayer (FFML) neural network (NN) and learning vector quantization (LVQ) NN. It is found that the classification performance of PNN is better than both FFML and LVQ. |
| Starting Page | 1 |
| Ending Page | 1 |
| File Size | 254356 |
| Page Count | 1 |
| File Format | |
| ISBN | 9781424438105 |
| DOI | 10.1109/PSCE.2009.4840264 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-03-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Event detection Vector quantization Power quality Neural networks Feature extraction Discrete event simulation Feedforward neural networks Multi-layer neural network |
| Content Type | Text |
| Resource Type | Article |
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