Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm-Reference-Cited by-同舟云学术

Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

Author:

Kari Tusongjiang¹,Gao Wensheng¹,Zhao Dongbo²,Abiderexiti Kaherjiang³,Mo Wenxiong⁴,Wang Yong⁴,Luan Le⁴

Affiliation:

1. Department of Electrical EngineeringTsinghua UniversityBeijingPeople's Republic of China

2. Argonne National LaboratoryIllinoisUSA

3. Department of Computer Science and TechnologyTsinghua UniversityBeijingPeople's Republic of China

4. Guangzhou Power Supply BureauChina Southern Power GridGuangzhouPeople's Republic of China

Funder

Ministry of Science and Technology of the People's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

Reference38 articles.

2. IEC 60597: ‘Guide for the sampling of gases and of oil‐filled electrical equipment and for the analysis of free and dissolved gas’ 2005

3. IEC 599: ‘Interpretation of the analysis of gases in transformers and other oil‐filled electrical equipment in service’ 1991

4. IEEE and IEC codes to interpret incipient faults in transformers using gas in oil analysis;Rogers R.R.;IEEE Trans. Electr. Insul.,1978

5. IEEE C57.104: ‘IEEE guide for the interpretation of gases generated in oil‐immersed transformer’ 2008

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