Abstract
Retrofitting condition monitoring systems to aging plant can be problematic, since the particular signature of normal behavior will vary from unit to unit. This paper describes a technique for anomaly detection within the context of the conditions experienced by an in-service transformer, such as loading, seasonal weather, and network configuration. The aim is to model the aged but normal behavior for a given transformer, while reducing the potential for anomalies to be erroneously detected. The paper describes how this technique has been applied to two transmission transformers in the U.K. A case study of 12 months of data is given, with detailed analysis of anomalies detected during that time.
| Original language | English |
|---|---|
| Pages (from-to) | 2556–2564 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Power Delivery |
| Volume | 25 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Oct 2010 |
Keywords
- anomaly detection
- multivariate data
- transformer monitoring
- monitoring
- power transformers
- fault diagnosis
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Cite this
Catterson, V. M., McArthur, S. D. J., & Moss, G. (2010). Online conditional anomaly detection in multivariate data for transformer monitoring. IEEE Transactions on Power Delivery, 25(4), 2556–2564. https://doi.org/10.1109/TPWRD.2010.2049754