Fault Diagnosis of Power Transformer Based on Adaptive Differential Evolution and Least Square Support Vector Machine

Zi Pin Li; Hui Peng

doi:10.4028/www.scientific.net/AMR.791-793.912

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 791-793Fault Diagnosis of Power Transformer Based on...

Fault Diagnosis of Power Transformer Based on Adaptive Differential Evolution and Least Square Support Vector Machine

Abstract:

Least square support vector machine (LS-SVM) can solve small sample, high-dimensional and non-linear multi-classification problem well, so it is applicable to the power transformer fault diagnosis. However, the parameters of LS-SVM have significant effect on the classification results.In this paper, the adaptive differential evolution algorithm (ADE) is applied to optimize the parameters of LS-SVM. The scaling factor and crossover rate are adjusted dynamically in the whole evolution process, so the robustness of the algorithm is improved greatly. The optimized LS-SVM is applied to fault diagnosis of power transformer, the results obtained demonstrate superiority of the proposed approach.

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Periodical:

Advanced Materials Research (Volumes 791-793)

Pages:

912-916

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.791-793.912

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Online since:

September 2013

Authors:

Zi Pin Li, Hui Peng

Keywords:

Adaptive Differential Evolution Algorithm, Fault Diagnosis, Least Squares Support Vector Machines (Ls-SVM), Parameter Optimization, Power Transformer

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