Prediction of transformer top oil temperature based on improved weighted support vector regression based on particle swarm optimization

A support vector regression (SVR) based on particle swarm optimization (PSO) is proposed to estimate the top oil temperature of transformer. This model establishes SVR model based on sample data such as ambient temperature, transformer load, and top oil temperature of transformer. The model analyzes the relationship between the top oil temperature of transformer and other factors, establishes the support vector hyperplane according to 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 support vector machine, the error between this area and the actual oil temperature of the top layer of transformer is minimized, and the top-oil temperature prediction model has the highest prediction accuracy. PSO is used to optimize the penalty factor and relaxation factor in SVR model. The kernel function is improved by principal component analysis to optimize the support vector regression model. Compared with particle swarm optimization(pso) support vector machine(SVM), which considers the weight of data feature quantity, the prediction accuracy is higher.This model uses the advantages of support vector regression method, such as not requiring a large number of samples, not involving probability measure, and being able to deal with multi-dimensional influencing factors, etc.It 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.