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Paper Abstract and Keywords
Presentation 2023-05-12 17:20
Fault Location using Deep Learning for TDR Waveforms in Overhead Distribution Systems with Few Branches
Tohlu Matsushima, Daiki Nagata, Yuki Fukumoto (Kyutech), Takashi Hisakado (Kyoto Univ), Uki Kanenari, Tsuyoshi Iinuma, Yusuke Nishihiro (Kansai Transmission and Distribution, Inc.), Shin Toguchi (DAIHEN Corporation) EMCJ2023-11
Abstract (in Japanese) (See Japanese page) 
(in English) Equipment using the TDR method is being developed to accelerate detection of faults in overhead distribution systems.
However, on a line with branches, the first reflected wave alone cannot be used to locate fault points. The accuracy of fault point detection is consequently reduced.
Therefore, we analyzed TDR waveforms using deep learning and examined whether it would be possible to locate fault points using information that is difficult to identify, such as multiple reflections.
Since it is difficult to obtain a large amount of measured TDR waveforms of faults, a fault point identification system was created by collecting and learning training data through circuit simulation using an equivalent circuit model.
The accuracy was evaluated by inputting the TDR waveforms of actual accidents, and it was found that fault point detection was possible with an error of less than approximately 100 m.
Furthermore, since the accuracy of the identification depends on the accuracy of the equivalent circuit model, the authors proposed a method to suppress over-learning by adding variability to the training data.
Keyword (in Japanese) (See Japanese page) 
(in English) TDR method / distribution system / fault detection / deep learning / transmission line / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 20, EMCJ2023-11, pp. 25-30, May 2023.
Paper # EMCJ2023-11 
Date of Issue 2023-05-05 (EMCJ) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee EMCJ IEE-EMC IEE-SPC  
Conference Date 2023-05-12 - 2023-05-12 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) EMC 
Paper Information
Registration To EMCJ 
Conference Code 2023-05-EMCJ-EMC-SPC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fault Location using Deep Learning for TDR Waveforms in Overhead Distribution Systems with Few Branches 
Sub Title (in English)  
Keyword(1) TDR method  
Keyword(2) distribution system  
Keyword(3) fault detection  
Keyword(4) deep learning  
Keyword(5) transmission line  
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1st Author's Name Tohlu Matsushima  
1st Author's Affiliation Kyushu Institute of Technology (Kyutech)
2nd Author's Name Daiki Nagata  
2nd Author's Affiliation Kyushu Institute of Technology (Kyutech)
3rd Author's Name Yuki Fukumoto  
3rd Author's Affiliation Kyushu Institute of Technology (Kyutech)
4th Author's Name Takashi Hisakado  
4th Author's Affiliation Kyoto University (Kyoto Univ)
5th Author's Name Uki Kanenari  
5th Author's Affiliation Kansai Transmission and Distribution, Inc. (Kansai Transmission and Distribution, Inc.)
6th Author's Name Tsuyoshi Iinuma  
6th Author's Affiliation Kansai Transmission and Distribution, Inc. (Kansai Transmission and Distribution, Inc.)
7th Author's Name Yusuke Nishihiro  
7th Author's Affiliation Kansai Transmission and Distribution, Inc. (Kansai Transmission and Distribution, Inc.)
8th Author's Name Shin Toguchi  
8th Author's Affiliation DAIHEN Corporation (DAIHEN Corporation)
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Speaker Author-1 
Date Time 2023-05-12 17:20:00 
Presentation Time 25 minutes 
Registration for EMCJ 
Paper # EMCJ2023-11 
Volume (vol) vol.123 
Number (no) no.20 
Page pp.25-30 
#Pages 6 
Date of Issue 2023-05-05 (EMCJ) 

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