First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers

Jakub Waikat, Amel Jelidi (), Sandro Lic, Georgios Sopidis, Olaf Kähler, Anna Maly, Jesús Pestana (), Ferdinand Fuhrmann and Fredi Belavić
Additional contact information
Jakub Waikat: Siemens Energy Austria GmbH, 1210 Wien, Austria
Amel Jelidi: Pro2Future GmbH, 8010 Graz, Austria
Sandro Lic: Pro2Future GmbH, 8010 Graz, Austria
Georgios Sopidis: Pro2Future GmbH, 8010 Graz, Austria
Olaf Kähler: Joanneum Research, 8010 Graz, Austria
Anna Maly: Joanneum Research, 8010 Graz, Austria
Jesús Pestana: Pro2Future GmbH, 8010 Graz, Austria
Ferdinand Fuhrmann: Joanneum Research, 8010 Graz, Austria
Fredi Belavić: Austrian Power Grid, 1220 Wien, Austria

Energies, 2024, vol. 17, issue 5, 1-26

Abstract: Transformers are a very important asset in the electrical transmission grid, and they can suffer from destructive events—e.g., rare transformer fires. Unfortunately, destructive events often lead to a lack of data available for investigators during post-event forensics and failure analysis. This fact has motivated our design and implementation of a robotic multi-sensor platform and cloud backend solution for the lifecycle monitoring, inspection, diagnostics, and condition assessment of transformers. The robotic platform collects data from specific viewpoints around the transformer during operation and at specific relevant lifecycle milestones of the transformer (e.g., at the factory acceptance test) in an automated, repetitive, precise, and reliable manner. The acquired data are stored in the cloud backend, which also provides computing resources and data access to relevant in- and off-premises services (e.g., respectively, SCADA systems, and weather reports). In this paper, we present the results of our first measurement campaign to showcase the value of our solution for transformer lifecycle monitoring, for anomaly detection, and as a crucial tool for post-event forensics in the case of destructive events.

Keywords: transformer; lifecycle-monitoring; electric transmission grid; service robotics; anomaly detection; internet of things; AI (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/5/1152/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/5/1152/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:5:p:1152-:d:1347958

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().