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计算机系统应用:2020,29(9):131-135
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基于图神经网络的配电网故障预测
(1.中国科学院大学 计算机与控制学院, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168;3.国网辽宁省电力有限公司, 沈阳 110168)
Accident Prediction of Power Distribution Network Based on Graph Neural Network
(1.School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.State Grid Liaoning Electric Power Co. Ltd., Shenyang 110168, China)
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投稿时间:2020-01-06    修订日期:2020-01-22
中文摘要: 在实际应用场景的配电网故障占电网总故障的80%以上,并且配电网故障的预测一直以来都是比较困难的课题.本文在国家电网提出“泛在物联”的号召下,分析了学者们在此问题上的研究成果后,结合图神经网络的思想提出了一种基于图神经网络的配电网故障预测方法.参考了现在常用的图神经网络设计框架,详细的设计了节点信息汇集函数、预测函数和损失函数,并根据算法流程测试选定了合理的深度参数.算法充分考虑了相连节点间的互相影响,使用真实的电网运行数据对在该课题上常用的其它两种算法的横向比较,实验表明算法在精确度上提高了3.0%,并具有更强的鲁棒性.
Abstract:Power distribution network accident in actual application scenarios account for more than 80% of total grid accident, and the prediction of power distribution network accident has always been a difficult issue. This study, under the call of “Ubiquitous IoT” proposed by the State Grid, analyzes the research results of scholars on this issue, and proposes an accident prediction method for power distribution network based on graph neural network with the idea of graph neural network. Referring to the commonly used graph neural network design framework, the node information aggregation function, prediction function, and loss function are designed in detail, and reasonable depth parameters are selected according to the algorithm flow test. The algorithm fully considers the mutual influence between connected nodes, and uses the real grid operation data to compare the two other algorithms commonly used in this field. Experiments show that the proposed algorithm improves the accuracy by 3.0% and is more robust.
文章编号:7516     中图分类号:    文献标志码:
基金项目:国家科技重大专项(2017ZX01030-201)
引用文本:
杨华,李喜旺,司志坚,张晓.基于图神经网络的配电网故障预测.计算机系统应用,2020,29(9):131-135
YANG Hua,LI Xi-Wang,SI Zhi-Jian,ZHANG Xiao.Accident Prediction of Power Distribution Network Based on Graph Neural Network.COMPUTER SYSTEMS APPLICATIONS,2020,29(9):131-135

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