Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

Explore

Online conditional anomaly detection in multivariate data for transformer monitoring

Catterson, Victoria M. and McArthur, Stephen D. J. and Moss, Graham (2010) Online conditional anomaly detection in multivariate data for transformer monitoring. IEEE Transactions on Power Delivery, 25 (4). 2556–2564. ISSN 0885-8977

[img]
Preview
Text (Catterson-etal-IEEE-TOPD-2010-Online-conditional-anomaly-detection-in-multivariate-data-for)
Catterson_etal_IEEE_TOPD_2010_Online_conditional_anomaly_detection_in_multivariate_data_for.pdf - Accepted Author Manuscript

Download (2MB) | Preview

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.