MATEC Web Conf.
Volume 63, 20162016 International Conference on Mechatronics, Manufacturing and Materials Engineering (MMME 2016)
|Number of page(s)||5|
|Section||Mechatronic and Application Engineering|
|Published online||12 July 2016|
Study on Prediction of Top Oil Temperature for Transformers Based on Bayesian Network Model
School of Electrical Engineering, South West Jiaotong University, Chengdu 610031, China
a LI Ran: firstname.lastname@example.org
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.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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