A kind of top oil temperature model of transformer of the improved weighted support vector regression（SVR）based on particle swarm optimization（PSO）is set up

which can accurately estimate the top oil temperature of transformer. This model sets up SVR model in accordance with such sample data as ambient temperature

transformer load and top oil temperature of transformer

analyzes the relationship between the top oil temperature of transformer and other factors

sets up the support vector hyperplane in accordance with different influencing factors and limits the prediction of the top oil temperature of transformer to a reasonable interval. According to the choice of penalty factor and relaxation factor of the support vector machine

the error between this interval and the actual oil temperature of the top layer of transformer is minimized so to keep the predicted model accuracy

for which the top oil temperature of the transformer is as the prediction function

is the highest. During the setup of SVR model

the particle swarm algorithm is used to optimize the penalty factor and relaxation factor so to keep the prediction result of the SVR model reach optimal. The kernel function is improved by principal component analysis so to optimize the support vector regression model. Compared with particle swarm optimization support vector machine（SVM）considering the weight of data feature quantity

the accuracy of the prediction result is higher. This model uses such advantages of support vector regression method as not requiring a large number of samples

not involving probability measure and being able to deal with multi-dimensional influencing factors etc

can provide accurate top-oil temperature prediction results in case of insufficient short-term prediction data of transformer oil temperature or more dimensions of oil temperature related data collected.