DocumentCode
2115225
Title
Notice of Retraction
The Application of the IGA in Transformer Fault Diagnosis Based on LS-SVM
Author
Li Yanqing ; Huang Huaping ; Li Ningyuan ; Xie Qing ; Lu Fangcheng
Author_Institution
Dept. of Electr. Eng., North China Electr. Power Univ., Baoding, China
fYear
2010
fDate
28-31 March 2010
Firstpage
1
Lastpage
4
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
LS-SVM (least square support vector machines) is applied to solve the practical problems of small samples and non-linear prediction better, and it is suitable for the DGA in power transformers. But in this model, the selecting of the parameters, and , impact on the result of the diagnosis greatly, so it is necessary to optimize these parameters. The IGA (improved genetic algorithm) is applied in this paper to make an optimization of these parameters about LS-SVM. The IGA uses the encoding mechanism; it generates the initial population randomly, expends the search space fast, stabilizes the diversity of the individuals in population, and effectively improves the global search ability and convergence speed. Finally, the optimized LS-SVM is used to analysis multiple sets of oil chromatogram data of transformers, the results show that the parameters of LS-SVM are effectiveness optimized by IGA, and the accuracy of fault diagnosis effectively improved.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
LS-SVM (least square support vector machines) is applied to solve the practical problems of small samples and non-linear prediction better, and it is suitable for the DGA in power transformers. But in this model, the selecting of the parameters, and , impact on the result of the diagnosis greatly, so it is necessary to optimize these parameters. The IGA (improved genetic algorithm) is applied in this paper to make an optimization of these parameters about LS-SVM. The IGA uses the encoding mechanism; it generates the initial population randomly, expends the search space fast, stabilizes the diversity of the individuals in population, and effectively improves the global search ability and convergence speed. Finally, the optimized LS-SVM is used to analysis multiple sets of oil chromatogram data of transformers, the results show that the parameters of LS-SVM are effectiveness optimized by IGA, and the accuracy of fault diagnosis effectively improved.
Keywords
fault diagnosis; genetic algorithms; least squares approximations; power transformers; support vector machines; IGA; convergence speed; global search ability; improved genetic algorithm; least square support vector machines; oil chromatogram; transformer fault diagnosis; Convergence; Dissolved gas analysis; Encoding; Fault diagnosis; Genetic algorithms; Least squares methods; Oil insulation; Petroleum; Power transformers; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location
Chengdu
Print_ISBN
978-1-4244-4812-8
Type
conf
DOI
10.1109/APPEEC.2010.5449311
Filename
5449311
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