Transformer hot spot temperature prediction based on basic operator information

D.P Rommel,D Di Maio,T Tinga

doi:10.1016/j.ijepes.2020.106340

D.P Rommel, D Di Maio + Show 1 more

Open Access

https://doi.org/10.1016/j.ijepes.2020.106340

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Abstract

A power transformer is an important component in power trains and electrical distribution networks. Predicting its life time is desirable, especially, if a zero downtime policy is applied. However, end customers often have to deal with a lack of information and cannot always use the established methods for life time prediction. Therefore, the present paper provides an alternative way to calculate the hot spot temperature and thus, the life time of power transformers based on limited information, i.e. transformer rating information and rms current and voltage measurements (including phase angles). The transformer hot spot temperature is derived from the transformer losses and a virtual twin. Therefore, the paper provides methods i) to evaluate the separate transformer losses, i.e. core, winding and stray losses, ii) to create a simple virtual transformer twin and iii) to calculate the temperature distribution in the transformer windings and thus, the hot spot temperature. The methods are applied to one phase of a 154 kV, 15MVA power transformer. It is shown that the calculated losses and hot spot temperature matches with winding measurements available in literature.

Highlights

Surveys of failures in wind turbine systems evaluated during the last decades have shown that power transformers have non-negligible failures rates and downtimes [1]
End customers and consultancy companies often deal with a lack of information and cannot always use the established methods for life time prediction
The present paper provides an alternative way to evaluate the life time of power transformers based on limited information

Summary

Introduction

Surveys of failures in wind turbine systems evaluated during the last decades have shown that power transformers have non-negligible failures rates and downtimes [1]. To achieve maintenance with zero or, at least, close to zero downtime maintenance prediction becomes important This means that end customers (transformer owners and operators) or consultancy companies seek for methods to evaluate the remaining useful life time based on actual operating conditions [2;3] for a similar approach for bearings) These two stakeholders normally deal with the problem that only limited information about the component and actual operating conditions are available to them. Methods are needed that can predict the remaining useful life time of components based on limited information, for example, based on component ratings information and simple measurements only

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