Notice of RetractionThe Application of the IGA in Transformer Fault Diagnosis Based on LS-SVM

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.