Transformer Fault Diagnosis Based on Stacking Ensemble Learning. Issue 12 (2nd October 2020)

• Record Type: Journal Article
• Authors: Wang, Xue; Han, Tao
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 15:Issue 12(2020)
• Page Start: 1734
• Page End: 1739
• Publication Date: 2020-10-02
• Subjects: transformer -- DGA -- fault diagnosis -- random forest -- Stacking algorithm; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.23247
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : Dissolved gas analysis is an important way to diagnose transformer faults. Compared with the method of establishing a single classifier based on artificial intelligence for diagnosis, ensemble learning (EL) can combine multiple classifiers to achieve stronger generalization ability and better diagnostic performance. But the traditional EL belongs to homogenous ensemble in which the base learners are based on the same algorithm, so this kind of EL method lacks the differences among the base learners, as well as systematic combination strategy. For this problem, in the paper the Stacking ensemble strategy is applied to fault diagnosis. Multilayer perceptron, k‐nearest neighbor, decision tree and support vector machine are used as component learners, and random forest algorithm is used as a combination strategy to establish a Stacking diagnosis model. In addition, homogenous ensemble methods are applied to the above four algorithms. In the method, the content of five characteristic gases are taken as the input characteristic parameters. Primary diagnostic results can be obtained with each base classifier. Then the meta‐learner random forest model organizes the base classifiers, and uses the primary diagnostic output as the input of the meta‐learner for secondary diagnosis to get the final diagnosis. The experimental results show that the ensemble of multiple heterogeneous component learners can enhance the generalization ability of the model, and the diagnostic accuracy is better than single classifier and the homogenous ensemble classifier. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.