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

Aizpurua, J. I. and Stewart, B. G. and McArthur, S. D. J. and Lambert, B. and Cross, J. G. and Catterson, V. M. (2019) Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index. Applied Soft Computing, 85. 105530. ISSN 1568-4946 (https://doi.org/10.1016/j.asoc.2019.105530)

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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.

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