The top oil temperature for transformer has a great influence on transformer’s operational life and load capacity, therefore, it is important to predict the top oil temperature. On the basis of analyzing and summarizing the main impacts on the top oil temperature, an idea is proposed to predict the top oil temperature by means of Bayesian network, and Bayesian network model is established. The model takes active power, reactive power, load current, ambient temperature and previous time oil temperature as its quantitative indicators, and trains the sample data to find out the probability distribution between various factors. The model is verified according to data collected from the transformer of SSZ11-50kV/220. The results show that the relative error between predictive value and measured value is small, which can be accepted completely in engineering. Therefore, Bayesian network is reasonable and can be widely applied to forecast the top oil temperature.
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