Analysis and Evaluation Model of Smart Grid Operation State Basedon Graph Neural Network
[1]LIU Huilin,FAN Ruiming,CHENG Dachuang,et al.Analysis and Evaluation Model of Smart Grid Operation State Basedon Graph Neural Network[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):122-128.[doi:10.13705/j.issn.1671-6833.2024.06.017]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
45
Number of periods:
2024 06
Page number:
122-128
Column:
Public date:
2024-09-25
- Title:
- Analysis and Evaluation Model of Smart Grid Operation State Basedon Graph Neural Network
- Keywords:
- stability assessment; fault location; data filling; long short-term memory network; graph neural network
- CLC:
- TM712
- DOI:
- 10.13705/j.issn.1671-6833.2024.06.017
- Abstract:
- The safe operation of smart grid was the primary premise to ensure continuous and efficient power supply. Therefore, a graph neural network (GNN) based power system operation state analysis and evaluation modelwas proposed. Firstly, long short-term memory network was used to fill missing data, to ensure that the model hadgood performance in stability assessment and fault location. Secondly, a binary classifier for evaluating the stablestate of power grid operation and a multi classifier for locating faulty components were designed based on GNN. Dueto the ability of the proposed model to fully explore the spatiotemporal characteristics of power grid operation data,the proposed model exhibited superior performance compared to other methods under different measurement conditions. Experimental results showed that when the time series length of data was 0. 1 seconds, the stability assessment and fault location accuracy of the proposed model were 0. 985 5 and 0. 981 4, respectively, and higher thanthe comparative models. When only half of the component data can be measured, the accuracy of the proposedmodel for stability assessment, bus fault location, and generator fault location were 0. 998 0, 0. 960 9, and0. 981 2, respectively, and higher than the comparative models.
- References:
-
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Last Update:
2024-09-29