Sinaga, Herman Halomoan and Phung, Toan and Blackburn, Trevor (2011) Neuro fuzzy recognition of ultra-high frequency partial discharges in transformers. IEEE 2010 Conference Proceedings IPEC. ISSN 1947-1270
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Abstract
In this paper, partial discharge (PD) signals in the ultra-high frequency (UHF) range were investigated. A spectrum analyzer functioning in the zero span mode was applied to capture and record the PD signal component at a specific frequency over a time interval. Different PD sources produce different PD patterns, thus it is possible to recognize the PD sources from the captured PD patterns. Here, the PD patterns produced by 3 different laboratory models representing defects in transformer windings (void, floating metal, and surface discharge) are recorded and analyzed. From the PD pattern data, 6 features are extracted using 3 statistical parameters, i.e. mean, skewness and kurtosis for both positive and negative voltage halfcycles. The 6 features were used to recognize the PD sources by applying neuro fuzzy method to classify the PD pattern. ANFIS, a MatLab function, was used to train the fuzzy inference system (FIS). The trained FIS was then used to recognize the source of the PDs. Result shows the trained FIS has a high success rate to recognize and thus classify the PD sources.
| Item Type: | Article |
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Teknik (FT) > Prodi Teknik Elektro |
| Depositing User: | DR. Herman H Sinaga |
| Date Deposited: | 11 Apr 2023 07:26 |
| Last Modified: | 11 Apr 2023 07:26 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/50224 |
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