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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Izzularab, M.A. Aly, G.E.M. Mansour, D.A. |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Electr. Eng., Menoufiya Univ., Shebin El-Kom, Egypt (Izzularab, M.A.) |
| Abstract | In this paper, a new artificial intelligence technique is proposed to detect incipient faults and cellulose degradation in power transformers using dissolved gas analysis. The proposed technique is based on a combination between neural networks and fuzzy logic theory. Incipient faults diagnosis is based on hydrocarbon gases as an input while cellulose degradation detection is based on carbon monoxide and carbon dioxide. The capabilities of the proposed diagnostic system have been verified through practical test data collected from the Egyptian electricity network. A comparison between the proposed technique and reported methods is carried out. |
| Sponsorship | IEEE Dielectrics and Elec. Insulation Soc |
| Starting Page | 767 |
| Ending Page | 770 |
| File Size | 247699 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780383486 |
| DOI | 10.1109/ICSD.2004.1350545 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-07-05 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fault diagnosis Degradation Artificial intelligence Fault detection Power transformers Dissolved gas analysis Artificial neural networks Fuzzy logic Hydrocarbons Gases |
| Content Type | Text |
| Resource Type | Article |
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