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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang Ke Chen Weirong Li Qi |
| Copyright Year | 2012 |
| Abstract | The fault identification of power system is of great significance in the event of failure This paper introduce a fault identification method based on multi-wavelet packet and artificial neural network. Firstly, through the simulation of a two-500Kv power source transmission line on PSCAD/EMTDC, the variety of fault signals is generated in different conditions. Then, these fault signals are decomposed appropriately by multi-wavelet packets. Therefore, the energy features of the fault signals in each frequency band are obtained. BP neural network is trained by suitable training sample. Finally, each fault type can be automatically identified through combining multi-wavelet packet and BP neural network. From the results, the method is effective to identify the fault of high-voltage AC transmission line in power systems. |
| Starting Page | 1457 |
| Ending Page | 1462 |
| File Size | 436743 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457721205 |
| DOI | 10.1109/ISdea.2012.421 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-01-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Fault diagnosis Resistance Fault classification Power transmission lines Multi-wavelet BP neural network Grounding Circuit faults Biological neural networks |
| Content Type | Text |
| Resource Type | Article |
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