Entropy application in partial discharge analysis with non-intrusive measurement
Partial discharge (PD) occurs when insulation deterioration happens in electrical apparatus. It is often detected in order to evaluate the state of insulation. For metal-clad equipments, external sensors which are easy to install and interruption-free on operations are preferred. However, their perf...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/84742 http://hdl.handle.net/10220/12378 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Partial discharge (PD) occurs when insulation deterioration happens in electrical apparatus. It is often detected in order to evaluate the state of insulation. For metal-clad equipments, external sensors which are easy to install and interruption-free on operations are preferred. However, their performances are compromised by heavy noise. Although time-frequency (TF) spectrum provides much information to discriminate PDs and noises, automatic selection remains a tough issue in field application. Entropy, a measure of disorder, is applied in this paper to extract PD pulses automatically. This entropy-based algorithm is implemented and examined by two field-collected datasets. Practical results show that true PDs can be identified and extracted effectively. |
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