状态监测大数据存储及聚类划分研究

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Abstract In recent years, with the power transmission equipment condition monitoring continuously strengthen in the breadth and depth, more and more monitoring data were collected, and gradually formed big data of smart grid condition monitoring. However, how to effectively store and analyze condition monitoring big data is a key issue of big data applying in power transmission equipment condition monitoring fields. Based on cloud computing platform and considering characteristics of condition monitoring data, monitoring data will be combined a large mass of small files into sequence file and compressed storage, thereby improving the efficiency of storage and processing. For condition monitoring big data low-density characteristics, firstly fractal theory was used for dimensionality reduction monitoring data, and computing time domain and frequency domain feature quantity, in addition utilizing DBSCAN algorithm clustering the sample data and acquiring different cluster feature data; then integrating with cloud computing platform parallel data processing capability designed MapReduce algorithm for clustering condition monitoring big data, and contributing found valuable feature quantity from big data; finally, using cluster result of big data updated and enriched sample data. Experimental results show that this method can efficiently store and cluster condition monitoring big data, for improving the status of device evaluation and fault diagnosis has a certain role.

Key words： Condition monitoring big data cloud storage cluster

Received: 20 October 2013 Published: 22 May 2014

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TM769

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	Articles by authors
	Zhou Guoliang
	Song Yaqi
	Wang Guilan
	Zhu Yongli

Cite this article:

Zhou Guoliang,Song Yaqi,Wang Guilan等. Research of Condition Monitoring Big Data Storage and Clustering[J]. Transactions of China Electrotechnical Society, 2013, 28(2增): 337-344.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2013/V28/I2增/337

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