Improved information entropy weighted vague support vector machine method for transformer fault diagnosis

A combined model based on improved information entropy and vague support vector machine (IVSVM) is introduced into transformer fault diagnosis using dissolved gas analysis in oil (DGA). The improved information entropy method is used to obtain the weights of each gas and to weight the raw data, and...

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Bibliographic Details
Published in:High voltage 2022-06, Vol.7 (3), p.510-522
Main Authors: Zhang, Hongru, Sun, Jiaxiang, Hou, Kaining, Li, Qingquan, Liu, Hongshun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Carbon dioxide
Classification
Dissolved gases
Entropy
Entropy (Information theory)
Fault diagnosis
Fuzzy sets
Gas analysis
Gases
Lagrange multiplier
Machine learning
Neural networks
Support vector machines
Transformers
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Summary:A combined model based on improved information entropy and vague support vector machine (IVSVM) is introduced into transformer fault diagnosis using dissolved gas analysis in oil (DGA). The improved information entropy method is used to obtain the weights of each gas and to weight the raw data, and the processed training data and the corresponding fault types are inputted into the vague support vector machine (VSVM) model to obtain classifiers. Firstly, the training data are weighted by the improved information entropy method to discretise the original data from the mixed state for subsequent classifier training. Then, the vague set divides the events into true, false and unknown factors, which can optimise the sub‐interface of SVM and improve the accuracy of the boundary point classification. Finally, fault data from the literature and actual collections are selected for training and testing. By comparing with the widely used ratio method and artificial intelligence method, it can be concluded that the method described herein can effectively improve the accuracy of fault diagnosis. The result shows that this method has better applicability when facing actual fault type classification with higher data similarity.
ISSN:2397-7264
2397-7264
DOI:10.1049/hve2.12095