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Paper Abstract and Keywords
Presentation 2017-10-20 15:30
A Fundamental Study of Training Data Selection Method for Wind Turbine Health Management Using SCADA Data
Akihisa Yasuda (UT), Jun Ogata (AIST), Yoko Furusawa (UT), Masahiro Murakawa (AIST), Hiroyuki Morikawa, Makoto Iida (UT) R2017-47
Abstract (in Japanese) (See Japanese page) 
(in English) Wind turbines need to be stopped for a long period if the internal equipment breaks down. Therefore, it is important for the wind power business to detect the anomaly related to breakdown of the wind turbine quickly and to implement repair work for extending the life of the equipment. In this paper, assuming a system health monitoring method which uses data collected by SCADA (Supervisory Control And Data Acquisition) which is installed as a wind turbine standard equipment, we propose a method of extracting normal data from SCADA data using ideal power curve and evaluating the normal behavior of the data with change point detection.
Keyword (in Japanese) (See Japanese page) 
(in English) Wind Turbine / SCADA / Normal Behavior / Anomaly Detection / Machine Learning / Training Data / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 253, R2017-47, pp. 17-22, Oct. 2017.
Paper # R2017-47 
Date of Issue 2017-10-13 (R) 
ISSN Print edition: ISSN 0913-5685    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 R  
Conference Date 2017-10-20 - 2017-10-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To R 
Conference Code 2017-10-R 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Fundamental Study of Training Data Selection Method for Wind Turbine Health Management Using SCADA Data 
Sub Title (in English)  
Keyword(1) Wind Turbine  
Keyword(2) SCADA  
Keyword(3) Normal Behavior  
Keyword(4) Anomaly Detection  
Keyword(5) Machine Learning  
Keyword(6) Training Data  
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Keyword(8)  
1st Author's Name Akihisa Yasuda  
1st Author's Affiliation The University of Tokyo (UT)
2nd Author's Name Jun Ogata  
2nd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
3rd Author's Name Yoko Furusawa  
3rd Author's Affiliation The University of Tokyo (UT)
4th Author's Name Masahiro Murakawa  
4th Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
5th Author's Name Hiroyuki Morikawa  
5th Author's Affiliation The University of Tokyo (UT)
6th Author's Name Makoto Iida  
6th Author's Affiliation The University of Tokyo (UT)
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Speaker Author-1 
Date Time 2017-10-20 15:30:00 
Presentation Time 25 minutes 
Registration for R 
Paper # R2017-47 
Volume (vol) vol.117 
Number (no) no.253 
Page pp.17-22 
#Pages
Date of Issue 2017-10-13 (R) 


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