Examination of Insulation Diagnosis in Substation by Neural Network with Phase-resolved Partial Discharge Pattern Reconstruction
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- Fujioka Shunnya
- Kyushu Institute of Technology
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- Kawano Hideaki
- Kyushu Institute of Technology
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- Kozako Masahiro
- Kyushu Institute of Technology
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- Hikita Masayuki
- Kyushu Institute of Technology
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- Eda Osamu
- Tokyo Densetu Service Co., Ltd.
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- Yaguchi Shuhei
- Tokyo Densetu Service Co., Ltd.
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- Shiina Yasuharu
- Tokyo Densetu Service Co., Ltd.
Bibliographic Information
- Other Title
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- 位相分解部分放電パターン再構築とニューラルネットワークによる変電所の絶縁診断
- イソウ ブンカイ ブブン ホウデン パターン サイコウチク ト ニューラルネットワーク ニ ヨル ヘンデンショ ノ ゼツエン シンダン
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Abstract
<p>Several studies for partial discharge (PD) pattern recognition using artificial neural network (ANN) were reported in the early 1990s. Usually, in an actual field such as a substation, data on partial discharge is scarcely available, or even rare. In many cases, the power supply phase required for the PRPD pattern cannot be easily obtained. We propose an ANN method that shifts the phase in which the maximum signal intensity detected with PD sensors is generated and used it as training and input data, even for the few phases resolved PD data available in the field. This ANN method was applied to the PRPD pattern obtained in a practical field. As a result, it was shown that the discrimination rate between PD and noise was improved, and therefore the proposed ANN method was found to be effective.</p>
Journal
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- IEEJ Transactions on Fundamentals and Materials
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IEEJ Transactions on Fundamentals and Materials 142 (3), 94-100, 2022-03-01
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390291767478214656
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- NII Article ID
- 130008165874
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- NII Book ID
- AN10136312
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- ISSN
- 13475533
- 03854205
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- NDL BIB ID
- 032019965
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed