Storage and Query of Condition Monitoring Data in Smart Grid Based on Hadoop

With the development of smart grid, the amount of condition monitoring data would be huge and far beyond the scope of traditional condition monitoring system. Condition monitoring data has the features of the wide area, panoramic, mass, accurate and reliable, and the methods of conventional data storage and management are obviously difficult to adapt the needs. This paper proposes a processing method of condition monitoring data in smart grid based on Hadoop, in connection with the characteristics of condition monitoring in smart grid and combined with Hadoop cloud computing. A condition monitoring platform based on Hadoop is built and improved, to implement reliable storage and fast parallel processing of massive data.

[1]  Zhu Yongli,et al.  Information Platform of Smart Grid Based on Cloud Computing , 2010 .

[2]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[3]  Xue Li,et al.  Cyber Physical Power Systems: Architecture,Implementation Techniques and Challenges , 2010 .

[4]  Anthony K. H. Tung,et al.  MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters , 2011, IEEE Transactions on Knowledge and Data Engineering.

[5]  Wilson C. Hsieh,et al.  Bigtable: A Distributed Storage System for Structured Data , 2006, TOCS.

[6]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[7]  Kang Chen,et al.  Cloud Computing: System Instances and Current Research: Cloud Computing: System Instances and Current Research , 2010 .

[8]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[9]  Luis Rodero-Merino,et al.  A break in the clouds: towards a cloud definition , 2008, CCRV.

[10]  Chunming Rong,et al.  Performance Considerations of Data Acquisition in Hadoop System , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[11]  Zheng Wei,et al.  Cloud Computing:System Instances and Current Research , 2009 .

[12]  Bhavani M. Thuraisingham,et al.  Storage and Retrieval of Large RDF Graph Using Hadoop and MapReduce , 2009, CloudCom.

[13]  Rodney S. Tucker,et al.  Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport , 2011, Proceedings of the IEEE.

[14]  Rajkumar Buyya,et al.  Harnessing Cloud Technologies for a Virtualized Distributed Computing Infrastructure , 2009, IEEE Internet Computing.

[15]  Marko Vukolic,et al.  Reliable Distributed Storage , 2009, Computer.