Comparisons of transformer top oil temperature calculation models using support vector regression optimised by genetic algorithm

Abstract

In this study, the comparisons of three available transformer top-oil temperature models for use in dynamic loading are discussed – the modified IEEE clause 7 model, Swift model, and Susa model. Considering the non-linear character of these commonly used models, support vector regression (SVR) is applied for non-linear map between input onsite data and model outputs. Genetic algorithm is adopted to optimise-related parameters of SVR. Three metrics are used for measuring the accuracy of transformer thermal models: mean-squared error, correlation coefficient square, and the maximum error. It is presented that when the cooler condition of transformer is natural oil, natural air, Susa model has the best performance while the modified IEEE 7 model has the best performance when the transformer is forced oil, forced air cooled.