A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier

A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier

Dissolved gas analysis (DGA) is widely used to detect the incipient fault of power transformers. However, the accuracy is greatly limited by selection of DGA features and performance of fault diagnostic model. This paper proposed a fault diagnostic method integrating feature selection and diagnostic...

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Bibliographic Details
Main Authors: Jiake Fang, Hanbo Zheng, Jiefeng Liu, Junhui Zhao, Yiyi Zhang, Ke Wang
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Energies
Subjects:
power transformers
fault diagnosis
dissolved gases
improved social group optimization
Online Access:http://www.mdpi.com/1996-1073/11/8/1922

Internet

http://www.mdpi.com/1996-1073/11/8/1922

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