doi:10.16186/j.cnki.1673-9787.2020010019

Received:2020/01/07

Revised:2020/05/15

Published:2021/03/15

Study on transformer hot spot temperature real-time prediction method based onextreme learning machine

GAN Jingfu1, MA Minghan2, LI Yonggang2

1.Tangshan Power Supply Company， State Grid Jibei Power Co. ， Ltd. ， Tangshan 063000 ， Hebei， China;2.Department of Electric Power Engineering ，North China Electric Power University ，Baoding  071000 ，Hebei， China

Abstract:The load capacity and operation life of oil-immersed transformer are closely related to its hot spot temperature. Accurate prediction of transformer hot spot temperature is of great significance for monitoring transformer health， it is also of great significance for making dynamic capacity increase decision. In order to predict the transformer hot spot temperature， the transformer load rate， the environment temperature and the top layer oil temperature were taken as the characteristic values to fit the transformer hot spot temperature. BP neural network， Elman neural network and extreme learning machine （ ELM ） were used respectively. The results showed that the ELM had higher fitting degree and faster operation speed in the three methods. Then， the power load prediction model based on support vector regression （ SVR） was constructed. Seven kinds of transformer operation history data， such as temperature， humidity and weather type， were used in this model. Taking the prediction results as the pre-input of ELM model， a real-time prediction method of transformer hot spot temperature based on ELM was proposed. The operation data of a 220 kV oil-immersed transformer was taken as a sample to verif the proposed method， and the prediction results of this method could basically ensure that the error between the predicted value and the real value was within ± 1 °C. This indicated that the prediction method had good prediction accuracy.

Key words:oil-immersed transformer;hot spot temperature prediction;extreme learning machine;support vector regression

 基于极限学习机的变压器热点温度实时预测方法研究_甘景福.pdf