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 and 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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R2017-47 |
Conference Information |
Committee |
R |
Conference Date |
2017-10-20 - 2017-10-20 |
Place (in Japanese) |
(See Japanese page) |
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Topics (in Japanese) |
(See Japanese page) |
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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) |
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Keyword(1) |
Wind Turbine |
Keyword(2) |
SCADA |
Keyword(3) |
Normal Behavior |
Keyword(4) |
Anomaly Detection |
Keyword(5) |
Machine Learning |
Keyword(6) |
Training Data |
Keyword(7) |
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Keyword(8) |
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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 |
6 |
Date of Issue |
2017-10-13 (R) |
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