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.

Big Data Storage and Parallel Analysis of Grid Equipment Monitoring System

“…Z. Fadika et al [2] implemented a parallel data analysis algorithm using MapReduce framework, which can effectively improve the query efficiency of massive power monitoring data. D. W. Wang et al [7] achieved a parallel Bayesian classifier for transformer fault diagnosis based on MapReduce framework. This method provides higher diagnostic speed than that of a single-machine environment.…”