Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index

J. I. Aizpurua, B. G. Stewart, S. D. J. McArthur, B. Lambert, J. G. Cross, V. M. Catterson

Research output: Contribution to journalArticle

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The health index (HI) formulation is a pragmatic approach to combine multiple information sources and generate a consistent health state indicator for asset management planning. Generally, existing transformer HI methods are based on expert knowledge or data-driven models of specific transformer subsystems. However, the effect of uncertainty is not considered when integrating expert knowledge and data-driven models for the system-levelHI estimation. With the increased dynamic and non-deterministic engineering problems, the sources of uncertainty are increasing across power and energy applications, e.g. electric vehicles with new dynamic loads or nuclear power plants with de-energized periods, and transformer health assessment under uncertainty is becoming critical for accurate condition monitoring. In this context, this paper presents a novel soft computing driven probabilistic HI framework for transformer health monitoring. The approach encapsulates data analytics and expert knowledge along with different sources of uncertainty and infers a transformer HI value with confidence intervals for decision-making under uncertainty. Using real data from a nuclear power plant, the proposed framework is compared with traditional HI implementations and results confirm the validity of the approach for transformer health assessment.
Original languageEnglish
Article number105530
Number of pages15
JournalApplied Soft Computing
Volume85
DOIs
Publication statusPublished - 8 Jun 2019

Keywords

  • condition monitoring
  • transformer health monitoring
  • uncertainty
  • health index

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  • Research Output

    • 2 Citations
    • 3 Article
    • 2 Conference contribution book

    Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants

    Aizpurua, J. I., McArthur, S. D. J., Stewart, B. G., Lambert, B., Cross, J. G. & Catterson, V. M., 1 Jun 2019, In : IEEE Transactions on Industrial Electronics. 66, 6, p. 4726-4737 12 p.

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  • 12 Citations (Scopus)
    206 Downloads (Pure)

    Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

    Aizpurua, J. I., Catterson, V. M., Stewart, B. G., McArthur, S. D. J., Lambert, B., Ampofo, B., Pereira, G. & Cross, J. G., 19 Apr 2018, In : IEEE Transactions on Dielectrics and Electrical Insulation. 25, 2, p. 494-506 12 p.

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  • 17 Citations (Scopus)
    74 Downloads (Pure)

    Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

    Aizpurua, J. I., Catterson, V. M., Stewart, B. G., McArthur, S. D. J., Lambert, B. & Cross, J. G., 3 Dec 2018, In : IEEE Transactions on Systems Man and Cybernetics: Systems. p. 1-13 13 p.

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