1. Analysis and Evaluation Model of Smart Grid Operation State Based on Graph Neural Network.
- Author
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LIU Huilin, FAN Ruiming, CHENG Dachuang, PENG Long, ZHANG Guoliang, and ZHANG Zhaogong
- 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 model was proposed. Firstly, long short-term memory network was used to fill missing data, to ensure that the model had good performance in stability assessment and fault location. Secondly, a binary classifier for evaluating the stable state of power grid operation and a multi classifier for locating faulty components were designed based on GNN. Due to 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 than the comparative models. When only half of the component data can be measured, the accuracy of the proposed model for stability assessment, bus fault location, and generator fault location were 0. 998 0, 0. 960 9, and 0. 981 2, respectively, and higher than the comparative models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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