Izvorni znanstveni članak

https://doi.org/10.37798/2023722417

Monitoring Transformer Condition with MLP Machine Learning Model

Dino Žanić orcid.org/0009-0009-0043-9244 ; Croatian transmission system operator (HOPS)
Alan Župan orcid.org/0000-0002-5888-2443 ; Croatian transmission system operator (HOPS)

Sažetak

Failures of large power transformers in transmission system are always followed by significant costs, which is especially problematic given that they present an unplanned expenditure. In addition from disrupting financial plans, these events can lead to lower system reliability. This paper describes the development and potential application of transformer winding temperature model based on multilayer perceptron class of artificial neural networks. Model is built in Python programming language with data collected over the span of one year for a single transformer. Three input features (oil temperature, winding current and outside temperature) are used in the input layer, aiming to predict the winding temperature in the transformer. By comparing the predicted winding temperature with the actual measured winding temperature, insights into the transformers internal condition can be derived. To demonstrate the models proposed application, two types of transformer condition degradation are simulated and a set of certain indicators based on statistical measures are explored.

Ključne riječi

transformers; artificial neural networks; condition monitoring

Hrčak ID:

306212

URI

https://hrcak.srce.hr/306212

Datum izdavanja:

15.7.2023.

Posjeta: 387 *