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Energies 2019, 12(5), 960; https://doi.org/10.3390/en12050960

A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

1
Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
2
College of Power & Mechanical Engineering, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Received: 2 January 2019 / Revised: 5 March 2019 / Accepted: 7 March 2019 / Published: 12 March 2019
(This article belongs to the Section Smart Grids and Microgrids)
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Abstract

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing. View Full-Text
Keywords: transformer; fault diagnosis; dissolved gas analysis; twin support vector machines; chemical reaction optimization algorithm; restricted Boltzmann machine transformer; fault diagnosis; dissolved gas analysis; twin support vector machines; chemical reaction optimization algorithm; restricted Boltzmann machine
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Yuan, F.; Guo, J.; Xiao, Z.; Zeng, B.; Zhu, W.; Huang, S. A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine. Energies 2019, 12, 960.

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