+Advanced Search

Burning Fault Correlation Analysis and Prediction of Smart Meters in Operation in Wide-area Power Grid

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials

    Aiming at the burning loss of smart electricity meters, after correlation analysis of various factors, this paper proposes a burning fault prediction for smart meter based on XGBoost algorithm. Taking the data of a province from 2019 to 2020 as an example, the proposed method is tested and verified. Using the basic information data, operation data and environmental data, the proposed method is compared with the traditional algorithms such as KNN, naive Bayes and support vector machine. The results show that the burning fault prediction of the XGBoost algorithm is better than the traditional algorithms. The precision of the XGBoost is 91%, the recall is 66%, and F1-score is 76.51%. In the process of system deployment, LSTM algorithm is used to fill some missing values. The experimental results show that the model can accurately predict the burning fault of smart meter in low-voltage platform area.

    Cited by
Article Metrics
  • PDF:
  • HTML:
  • Abstract:
  • Cited by:
Get Citation
  • Received:
  • Revised:
  • Adopted:
  • Online: November 07,2022
  • Published: