Open AccessBayesian Network and Association Rules-based Transformer Oil Temperature Prediction
Author(s) -
Wei Rao,
Lipeng Zhu,
Shengli Pan,
Pei Yang,
Junfeng Qiao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1314/1/012066
Subject(s) - transformer oil , transformer , association rule learning , bayesian network , computer science , bayesian probability , data mining , artificial intelligence , machine learning , engineering , voltage , electrical engineering
The oil temperature prediction of transformer is very important for the operation stability and life evaluation of transformer. As the oil temperature prediction of transformer is still short of a comprehensive and efficient method with combining various information of transformer such as operation data and meteorological data, this paper proposed a Bayesian network and association rules-based transformer oil temperature prediction method, which can improve the prediction accuracy of RBF-NN for transformer oil temperature prediction. The proposed method first mines all association rules among transformer state data and transformer operation data and environmental meteorological information by combining the Bayesian network and the Apriori algorithm and then uses the association rules to improve the prediction accuracy of RBF-NN based on only transformer state data. A case study with a 500kV transformer is conducted to test the effectiveness of the proposed method, and the result shows that the proposed method can improve the prediction accuracy of RBF-NN about 10%.