Power Transformers: Predictive Maintenance

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Paper number
995
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Rodrigues, Sílvio, Jungle, Portugal
Verdelho, Maria Inês, EDP Distribuição, Portugal
Ribeiro, Ana Filipa, EDP Distribuição, Portugal
Cordeiro, Luís, EDP Inovação, Portugal
Abstract
This paper presents a holistic machine learning solution to forecast current and future Power Transformers (PT) health condition. Current PT monitoring and maintenance standards recommendations followed by utility companies depend mostly on the type of the PT and past results. We propose a health framework that is capable of accurately deriving key actionable insights. It leverages data from different sources and distinct business units. Current and future oil health conditions are forecasted and summarized in a Key Performance Indicator (KPI). The results show that key oil properties are forecasted up to five years in advance with high accuracy and that the predictions are robust to data outliers. Future oil interventions are then recommended based on the KPI value of the forecasted measurements. The proposed framework allows utility companies to save considerable resources by reducing and improving the scheduling of oil analyses, as well as increasing the quality of service (QoS) standards of energy grids.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/239
http://dx.doi.org/10.34890/465
ISSN
2032-9644
ISBN
978-2-9602415-0-1