Short‑term heavy overload forecasting method of distribution net line based on CNN‑GRU with Attention mechanism
Author:
Affiliation:

(1.Electric Power Engineering of Shanghai University of Electric Power, Shanghai 200090, China; 2.State Grid Shanghai Electrical Power Research Institute, Shanghai 200080, China; 3.State Grid Xinjiang Electrical Power Research Institute, Urumqi 830002, China)

Clc Number:

TM726

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    With the increase of electricity demand, the heavy overload of distribution network lines during the peak period of electricity consumption becomes more serious, which increases the potential threats on the safety of grid operation. The short?term forecast of the heavy overload state of distribution lines is of great significance for rationally arranging the operation mode, for dispatch management, and for the safety operation of the line during peak load periods. This paper proposes a short?term forecast method for the heavy overload state of lines and a prediction model that CNN?GRU hybrid neural network with Attention mechanism. The historical load rate of lines with high auto?correlation and meteorological factors are combined as the input features, which is further used to extract the valid features by the CNN. The GRU neural network is utilized to analyze and predict time series data. By using the Attention mechanism to reassign corresponding weights, the load rate regression prediction result can be outputed,which can be finally converted into the load level according to the load level division standard. The method in this paper is performed on a 10kV line in a certain district of Shanghai. The experimental results show that this prediction method is more suitable for line heavy overload prediction than the method using the classification prediction model with the same model structure but with load level as input.

    Reference
    Related
    Cited by
Get Citation

杨 秀,胡钟毓,田英杰,谢海宁,陈文涛.基于Attention机制的CNN‑GRU配网线路重过载短期预测方法[J].电力科学与技术学报英文版,2023,38(1):201-209. YANG Xiu, HU Zhongyu, TIAN Yingjie, XIE Haining, CHEN Wentao. Short‑term heavy overload forecasting method of distribution net line based on CNN‑GRU with Attention mechanism[J]. Journal of Electric Power Science and Technology,2023,38(1):201-209.

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:
  • Revised:
  • Adopted:
  • Online: April 10,2023
  • Published: