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Determining appropriate data analytics for transformer health monitoring

Aizpurua, Jose Ignacio and Catterson, Victoria M. and Stewart, Brian G. and McArthur, Stephen D.J. and Lambert, Brandon and Ampofo, Bismark and Pereira, Gavin and Cross, James G. (2017) Determining appropriate data analytics for transformer health monitoring. In: 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies. American Nuclear Society, La Grange Park, pp. 1-11. (In Press)

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Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring.