Journal of Jishou University(Natural Sciences Edition)

• Electrical Technology and Communication • Previous Articles     Next Articles

Quantity Prediction of Distribution Network FaultsBased on Big Meteorological Data

HE Dian,TAN Dudu,HE Hailing,ZOU Sheng,ZHONG Jiacheng,WANG Tingting   

  1. (1.Changsha Power Company of State Grid,Changsha 410000,China;2.Hunan Power Supply Service Center (Metrology Center) of State Gird,Changsha 414004,China)
  • Online:2019-01-25 Published:2019-01-26


With historical data of distribution network faults and big data of temperature,rainfall,and lighting,a weather-sensitive neural network predicting modelis proposed.Based on the maximum correlation coefficient of faults and meteorological factors,the main meteorological factors are real-time adjusted.The number,type and trend of the faults are accurately predicted.The result provides qualitative and quantitative reference for staff and materials deployment.

Key words: meteorological factors, big data;distribution network fault, urgent repair order, neural network model, maximum information entropy principle

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