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

Author

Listed:
  • 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
    Institute of Pervasive Computing, Johannes Kepler University Linz (JKU), 4040 Linz, Austria)

  • Olaf Kähler

    (Joanneum Research, 8010 Graz, Austria)

  • Anna Maly

    (Joanneum Research, 8010 Graz, Austria)

  • Jesús Pestana

    (Pro2Future GmbH, 8010 Graz, Austria
    Institute of Computer Graphics and Vision, Graz University of Technology (TU Graz), 8010 Graz, Austria)

  • Ferdinand Fuhrmann

    (Joanneum Research, 8010 Graz, Austria)

  • Fredi Belavić

    (Austrian Power Grid, 1220 Wien, Austria)

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.

Suggested Citation

  • Jakub Waikat & Amel Jelidi & Sandro Lic & Georgios Sopidis & Olaf Kähler & Anna Maly & Jesús Pestana & Ferdinand Fuhrmann & Fredi Belavić, 2024. "First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers," Energies, MDPI, vol. 17(5), pages 1-26, February.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1152-:d:1347958
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    References listed on IDEAS

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    1. Yuqing Zhang & Weijian Zhang & Chengxuan Wu & Fengwu Zhu & Zhida Li, 2023. "Prediction Model of Pigsty Temperature Based on ISSA-LSSVM," Agriculture, MDPI, vol. 13(9), pages 1-16, August.
    2. Li, Tangrong & Sun, Xuchu, 2023. "Predicting stock market returns using aggregate credit risk," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1087-1103.
    3. Xinyue Duan & Jiaqiang Zuo & Jiadong Li & Yu Tian & Chuanyong Zhu & Liang Gong, 2023. "Prediction of Gas Hydrate Formation in the Wellbore," Energies, MDPI, vol. 16(14), pages 1-10, July.
    4. Yeong-Hwa Chang & Yu-Chen Hsieh & Yu-Hsiang Chai & Hung-Wei Lin, 2023. "Remaining-Useful-Life Prediction for Li-Ion Batteries," Energies, MDPI, vol. 16(7), pages 1-20, March.
    5. Jiashu Lou & Leyi Cui & Ye Li, 2022. "Bi-LSTM Price Prediction based on Attention Mechanism," Papers 2212.03443, arXiv.org, revised Jun 2023.
    6. Yanyan Liu & Keping Li & Dongyang Yan & Shuang Gu, 2023. "The prediction of disaster risk paths based on IECNN model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 163-188, May.
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