WHITEPAPER: Correlative Analyses Considering Different Parameters In Order To Increase The Reliability Of Transformer Health Assessment

Reliable transformers are essential for a reliable electrical network. Therefore the knowledge of the condition of an asset is extremely important. Offline condition assessment methods are established and used since decades with success. Nevertheless, they give a screenshot of the asset in the moment that the measurements are taken. Despite this, the development of the health condition can only be estimated and the development of incipient faults can be missed.  Nowadays besides offline methods, more comprehensive online monitoring approaches for the transformer fleets combined with analytic models and severity analyses are used in order to capture changing conditions in real time and to predict critical situations.

In order to efficiently assess the condition of a transformer, the failure mechanism, its associated monitoring parameter(s) and the dedicated analytic model must be known and must be considered in its completeness. Comparing of different parameters is important in order to achieve a holistic view on a specific asset condition.