Optimization of LS-SVM Parameters Using Genetic Algorithm to Improve DGA Based Fault Classification of Transformer- A Review
نویسنده
چکیده
This paper gives the comparison of different methodologies for the fault classification in transformer based on Dissolved Gas Analysis. This paper describes the Dissolved Gas Analysis based fault classification of Transformer using Least Square Support Vector Machine. The parameters of Support Vector Machine are optimized using Genetic Algorithm. Failure of a large power transformer not only results in the loss of very expensive equipment but it can cause significant collateral damage as well.
منابع مشابه
LS-SVM Parameter Optimization Using Genetic Algorithm To Improve Fault Classification Of Power Transformer
The 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. The selection of the parameters, impact on the result of the diagnosis greatly, so it is necessary to optimize these parameters. The parameters of Support Vector Machine are optimized using GA (Geneti...
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