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Intelligent Framework and System for Remote Monitoring and Prediction of Power Transformer Conditions

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 410)

Abstract

Power supply grids and power transformers are essential components for energy delivery and transformation. No doubt that equipment must be functioning not only without interruption but safely also. One class of devices, power transformers at various enterprises, belongs to long-lived exploited devices having a high cost of maintenance and replacement. For controlling the state and the functioning condition of the power transformer, various monitoring systems have been proposed. However, the vast majority of such systems perform operations for measuring and store monitoring parameters only. This paper proposes a novel approach for implementing remote monitoring with specific abilities to reduce accidents and increasing the reliability of power transformers equipment. We developed a framework and intelligent system for this purpose. One of the distinctive features of the proposed system architecture is the predictive ability of the power transformer state based on machine learning techniques.

Keywords

  • Intelligent decision support
  • Machine learning
  • Risk and hazard analysis
  • Process monitoring
  • Power transformer

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Acknowledgment

The reported study was funded by the Russian Foundation for Basic Research according to the research projects 19-01-246-a, 19-07-00329-a.

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Correspondence to Andrey Chernov .

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Chernov, A., Butakova, M., Kostyukov, A., Kartashov, O. (2022). Intelligent Framework and System for Remote Monitoring and Prediction of Power Transformer Conditions. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2021. Lecture Notes in Networks and Systems, vol 410. Springer, Cham. https://doi.org/10.1007/978-3-030-96196-1_29

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