DocumentCode
3581146
Title
Study of the key technologies of electric power big data and its application prospects in smart grid
Author
Jie Zhan ; Jinxin Huang ; Lin Niu ; Xiaosheng Peng ; Diyuan Deng ; Shijie Cheng
Author_Institution
State Grid of China Technol. Colleague, State Grid Corp. of China, Jinan, China
fYear
2014
Firstpage
1
Lastpage
4
Abstract
Application of big data techniques in power system will contribute to the sustainable development of power industry companies and the establishment of strong smart grid. This article introduces a universal framework of electric power big data platform, based on the analysis of the relationships among the big data, cloud computing and smart grid. Then key techniques of electric power big data is discussed in four aspects, including big data management techniques, big data analysing techniques, big data processing techniques and big data visualization techniques. Finally, the article presents three typical application examples of electric power big data techniques which are new and renewable energy integration, wind turbine condition monitoring and assessment and data base integrative backup for electric power enterprises.
Keywords
cloud computing; data analysis; data visualisation; power system analysis computing; power system measurement; renewable energy sources; smart power grids; wind turbines; big data analysing techniques; big data management techniques; big data processing techniques; big data visualization techniques; cloud computing; data base integrative backup; electric power big data; electric power enterprises; power industry companies; power system; renewable energy integration; smart grid; sustainable development; wind turbine condition monitoring; Big data; Cloud computing; Data visualization; Power system stability; Smart grids; Wind turbines; Big data; cloud computing; data integration; electric power big data; smart grid; visualization;
fLanguage
English
Publisher
ieee
Conference_Titel
Power and Energy Engineering Conference (APPEEC), 2014 IEEE PES Asia-Pacific
Type
conf
DOI
10.1109/APPEEC.2014.7066162
Filename
7066162
Link To Document