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.
|Title of host publication||10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies|
|Place of Publication||La Grange Park|
|Publisher||American Nuclear Society|
|Number of pages||11|
|Publication status||Accepted/In press - 5 Apr 2017|
|Event||10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017 - Hyatt Regency, San Francisco, United States|
Duration: 11 Jun 2017 → 15 Jun 2017
|Conference||10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017|
|Abbreviated title||NPIC and HMIT 2017|
|Period||11/06/17 → 15/06/17|
- data analytics
- prognostics and health management
- condition monitoring
Aizpurua, J. I., Catterson, V. M., Stewart, B. G., McArthur, S. D. J., Lambert, B., Ampofo, B., ... Cross, J. G. (Accepted/In press). Determining appropriate data analytics for transformer health monitoring. In 10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies (pp. 1-11). La Grange Park: American Nuclear Society.