Prediction of top oil temperature for oil-immersed transformer based on Kalman filter algorithm

Abstract

The oil temperature of the oil-immersed power transformer is a key factor. It has a great influence on the transformer operation life and load capacity, and a close relationship with the transformer top oil temperature. Transformer in service at high temperature and overload condition easy to cause early failure resulted from overheating of winding hot-spot temperature, and the top oil temperature is an important indicator of transformer hot-spot temperature. We proposed a differential equation, in order to predict accurately the transformer top oil temperature, for predicting the top oil temperature after taking the thermal dynamic effect of load and the environment temperature variations into account, and employed the Kalman filter method to build a real time estimation model for top oil temperature based on state equations and measurement equations. At last, we comparatively analysis the prediction accuracy of Kalman filter model and IEEE guidelines recommend model combined with online monitored values.