Electric Power ›› 2015, Vol. 48 ›› Issue (2): 111-116.DOI: 10.11930.2015.2.111

• Information and Communication • Previous Articles     Next Articles

Cloud-Computing Based Power Big Data Analysis Technology and Its Application

WU Kaifeng1, LIU Wantao2, LI Yanhu2, SU Yipeng2, XIAO Zheng1, PEI Xubin3, HU Songlin2   

  1. 1. State Grid Electric Power Research Institute, Beijing 100761, China;
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    3. State Grid Zhejiang Electric Power Company Limited, Hangzhou 310007, China
  • Received:2014-10-29 Online:2015-02-25 Published:2015-11-30
  • Supported by:
    The project is supported by Technology Project of the State Grid Corporation of China under Grant No.SG[2012]805.

Abstract: In order to deal with the bottlenecks of the performance and scalability of power data analysis in present big data era and meet the requirements of power production and marking systems, the advantages of cloud computing technology is firstly analyzed, then the architecture and key techniques of Hadoop-based power big data analysis system are proposed. The architecture combines the characteristics of power big data and the emerging cloud computing technology, and includes three key technologies, i.e., the distributed gridfile based multi-dimensional index, the automatic SQL to HiveQL translation tool and the hybrid data storage model, which can support data update operation. Therefore, the traditional power data analysis system can be improved and updated. The application of the system in the power information collection system in Zhejiang indicates that the performance of the system is improved by 5 times with only one-eighth of the hardware configuration as compared with the traditional system. This proves that the cloud computing technology can significantly improve the performance and reduce the cost of big data acquisition and analysis.

Key words: electric power, big data, cloud computing, electricity information, data acquisition, data analysis

CLC Number: