Parallel Fault Diagnosis of Power Transformer Based on MapReduce and K-Means

De Wen Wang; Xiao Jian Liu

doi:10.4028/www.scientific.net/AMM.494-495.813

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 494-495Parallel Fault Diagnosis of Power Transformer...

Parallel Fault Diagnosis of Power Transformer Based on MapReduce and K-Means

Abstract:

Fault diagnosis can insure the power transformer safety and economic operation, and the data mining is the key technology of fault diagnosis for power transformer. In order to achieve the fast parallel fault diagnosis for power transformer, we need to put cloud computing technology into the smart grid. We give a parallel method of K-means based on MapReduce framework on the Hadoop distributed systems cluster to diagnose operation state of power transformer. Finally, through transformer fault diagnosis experimentations of massive DGA data, the results indicate closely linear speedup with an increasing number of node computers.

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Periodical:

Applied Mechanics and Materials (Volumes 494-495)

Pages:

813-816

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.494-495.813

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Online since:

February 2014

Authors:

De Wen Wang*, Xiao Jian Liu

Keywords:

K-Mean, MapReduce, Parallel Fault Diagnosis, Power Transformer

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