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Architecture and Technology Implementation of Massive Data Based Distribution Network Operation Analysis System

1.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;2.China Electric Power Research Institute, Beijing 100192, China


Accurate faults prediction and potential risk scanning of distribution network through flexible analysis and application of multi-source heterogeneous data are meaningful to realize efficient and accurate operation analysis decision of distribution network. The overall structure and function design of a distribution network operation analysis system based on mass data are introduced, and the functions and analysis results of each module are demonstrated by an application example. The system integrates data such as geographic information system (GIS), marketing business application and distribution automation. By using improved machine learning algorithm and weak point identification method, the functions of data correlation analysis, fault risk level prediction and weak point identification of target distribution network are extended. It is beneficial for relevant departments to propose corresponding technologies and management methods to carry out the operation and maintenance of distribution network, improve the scientific and practicality of the existing distribution network analysis system, and ultimately lay the foundation for the informatization, intellectualization and leanization of distribution network operation analysis.



This work is supported by National Key R&D Program of China (No. 2017YFB0903000) and Beijing Municipal Natural Science Foundation (No. 3172039).

Get Citation
[1]ZHANG Wen, SHENG Wanxing, DU Songhuai, et al. Architecture and Technology Implementation of Massive Data Based Distribution Network Operation Analysis System[J]. Automation of Electric Power Systems,2020,44(3):147-153. DOI:10.7500/AEPS20190520009
  • Received:May 20,2019
  • Revised:August 19,2019
  • Adopted:
  • Online: February 14,2020
  • Published: