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浅谈大数据处理技术在智能电网中的应用 被引量:5

Discuss on Application of Large Data Processing Technology in Smart Grid
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摘要 我们生活的时代叫做数据化时代,它不仅是科技的时代,也是信息的时代。通过大数据来对信息进行储存、收集、分析、综合和输出,是当前时代的重要特征。随着经济的快速发展和科学技术的进步,电网已经进入智能化的成熟阶段,体现了电力、信息和业务三者高度融合的个性特征。由于大数据处理技术的发展与进步,智能电网的信息化建设也进入了新的阶段。大数据处理技术在智能电网中的应用,不仅促进了智能电网的建设,也在一定程度上增加了智能电网建设中的信息化,为智能电网的发展提供了更好的发展方向。 The times we live in is called data era,it is not only the era of science and technology,but also the era of information.Through large data on information storage,collection,analysis,synthesis and output,is an important feature of the current era.With the rapid development of economy and the progress of science and technology,the power grid has entered the mature stage of intelligence,which embodies the highly integrated personality characteristics of power,information and service.Due to the development and progress of large data processing technology,the informatization construction of the smart grid has entered a new stage.Application of data processing technology in the smart grid not only promotes the construction of smart grid,but also increases in the construction of smart grid information to a certain extent,which provides better development direction for the development of smart grid.
作者 骆杨阳 LUO Yang-yang(School of Electrical Engineering, Chongqing University, Chongqing 401331, China)
出处 《通信电源技术》 2017年第4期186-187,共2页 Telecom Power Technology
关键词 大数据 大数据处理技术 智能电网 big data big data processing technology smart grid
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  • 1曹一家,陈晓刚,孙可.基于复杂网络理论的大型电力系统脆弱线路辨识[J].电力自动化设备,2006,26(12):1-5. 被引量:219
  • 2.http://mirrors.cnnic.cn/apache/hadoop/common/hadoop一2.4.0/.
  • 3Mackey G, Sehrish S, Wang J. Improving metadata management for small files in HDFS [A~. IEEE International Conference on Cluster Computing and Workshops [C] . 2009 : 1 - 4.
  • 4Shvachko K, Kuang H, Radia S, et al. The hadoop distributed file system [A]. 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) [C]. IEEE, 2010:1 - 10.
  • 5Armbrust M, Fox A. Griffith R. Above the clouds: a Berkeley view of cloud computing [D]. UCB/EECS-2009-28, EECS De- partment, University of California, Berkeley, 2009.
  • 6Huang D, Shi X, Ibrahim S, et al. MR-Scope~ A real time tracing tool for MapReduce [A]. The MapReduce of HPDC [C]. 2010. 849-855.
  • 7Wang Y D, Que X Y, Yu W K, et al. Hadoop acceleration through network levitated merge [A~. Proceedings of 2011 Inter- national Conference for High Performance Computing Networking, Storage and Analysis [C]. Washington: IEEE Press, 2011. 1 -10.
  • 8Kossmann D, Kraska T, Loesing S, et al. Cloudy~ a modular zloud storage system [A]. Proc. of the 36th lnt' 1 Conf. on Very Large Data Bases [C]. Singapore: VLDB Endowment, ~010: 1533-1536.
  • 9Chang F, Dean J, Ghemawat S, et al. Bigtable~ A distributed storage system for structured data [A]. Proceedings of the 7th ~ymposium on Operating Systems Design and Implementation [OSDI) [C]. Seattle, USA, 2006= 205-218.
  • 10Yu Y, Chen X F, Gopalakishnan G, et al. Runtime model chec- king of multithreaded C/C+ q- programs [D2. University of Utah SaltLakeCity, UT84112, U. S. A. 2010.

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