Utility pole deterioration modeling by machine learning with big data of distribution facility inspection result

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Paper number
774
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Yamanaka, Masaru, Kansai Electric Power Co.-Inc., Japan
Tokunaga, Tatsuya, Kansai Electric Power Co. Inc., Japan
Matsuki, Tatsushi, Kansai Electric Power Co. Inc., Japan
Abstract
Asthe aging of power distribution facilities progresses, the needs of asset management increases.It is necessary to make a rational repair planconsideringa comprehensive judgment from the view point of safety, cost, and workforce. Therefore, it is important to predict the occurrence of equipment failure and to determine the timing of repair/replacement.To generate data that will contribute to effective asset management, we performed big data analysis using the large amount inspection data possessed by our company. And we have been working on generating deterioration models forfailure modes of utility pole.In this paper, we introduce the accuracy of thedeterioration models which wecreated and an example of items with large influence on the each failure mode.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/150
http://dx.doi.org/10.34890/304
ISSN
2032-9644
ISBN
978-2-9602415-0-1