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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chen Wei-gen Liang Liu-ming Du Lin Liu Jun Yue Yan-feng Zhao Jian-bao |
| Copyright Year | 2008 |
| Description | Author affiliation: Luoyang Power Supply Co., Luoyang (Yue Yan-feng; Zhao Jian-bao) || State Key Lab. of Power Transm. Equip.&Syst. Security & New Technol., Chongqing Univ., Chongqing (Chen Wei-gen; Liang Liu-ming; Du Lin; Liu Jun) |
| Abstract | Transformer faults are quite complicated phenomena and can occur due to a variety of reasons. There have been several methods for transformer fault synthetic diagnosis, but each of them has its own limitations in real fault diagnosis applications. In order to overcome those shortcomings in the existing methods, a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm (AGA) and an improved D-S evidence theory fusion technique is proposed in this paper. The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis. Based on the fusion mechanism of D-S evidence theory, the comprehensive reliability of evidence is constructed by considering the evidence importance, the outputs of the neural network and the expert experience. The new method increases the objectivity of the basic probability assignment (BPA) and reduces the basic probability assigned for uncertain and unimportant information. The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers. |
| Sponsorship | Nat. Natural Sci. Found. China |
| Starting Page | 666 |
| Ending Page | 671 |
| File Size | 180314 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424438235 |
| DOI | 10.1109/ICHVE.2008.4774023 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Transformer Wavelet Neural Network Fault Diagnosis Optimization methods Oil insulation Reliability theory Power transformers D-S Evidence Theory Petroleum Genetic algorithms Fault diagnosis Neural networks Adaptive Genetic Algorithm Information Fusion Testing |
| Content Type | Text |
| Resource Type | Article |
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