Oil temperature prediction of power transformers based on modified support vector regression machine

Power transformer is an important part of the entire power grid and the normal operation of the power transformer can ensure the normal operation of the entire power grid. The oil in the transformer plays a non-negligible role in the transformer. There are a lot of machine learning methods to predict oil temperature of power transformer. The work of this paper is to predict the oil temperature based on support vector regression machine (SVM) with three-phase power load, while particle swarm optimization (PSO) is employed for the model parameter optimization. As there are many influential factors for oil temperature prediction, confidence intervals are introduced to determine the prediction results. The experimental results show that the prediction accuracy reaches 90 with 85% confidence level. For the sample points falling outside the prediction interval, they can be regarded as the abnormal transformer status in time. The experimental results verified that the proposed oil temperature prediction method for power transformers based on modified SVM is effective and feasible.