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| Feature Extraction and Pattern Recognition of Corona Discharging Signals |
| Received:January 18, 2017 Revised:April 15, 2017 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/ issn.1672-9242.2017.04.012 |
| KeyWord:corona discharging feature extraction pattern recognition probabilistic neural network |
| Author | Institution |
| HU Xiao-feng |
Institute of Electrostatic and Electromagnetic Protection, Machine Engineering College, Shijiazhuang , China |
| LIU Wei-dong |
Institute of Electrostatic and Electromagnetic Protection, Machine Engineering College, Shijiazhuang , China |
| ZHOU Shuai |
Institute of Electrostatic and Electromagnetic Protection, Machine Engineering College, Shijiazhuang , China |
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| Abstract: |
| Objective To research methods for feature extraction and pattern recognition of corona discharge radiation signals. Methods Based on the analysis of signal feature extraction method, the signal feature of corona discharge radiation measured was extracted. The probabilistic neural network was adopted to identify corona discharge radiation signal target to test the effectiveness of the proposed feature extraction. Results The PNN with singular value as the input characteristics was overall better in effect and good in stability. Its correct rate of recognition of two kinds of different discharge radiation signals could be higher than 80%. When ten characteristics were input, the correct recognition rate reached the peak of the measured samples. The correct recognition rate of corona discharge was 96.7%. The correct recognition rate of spark discharge was 93.3%. Conclusion This method can basically meet the recognition and application of actual discharge signals. |
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