[1]刘慧林,范瑞明,程大闯,等.基于图神经网络的智能电网运行状态分析与评估[J].郑州大学学报(工学版),2024,45(06):122-128.[doi:10.13705/j.issn.1671-6833.2024.06.017]
　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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基于图神经网络的智能电网运行状态分析与评估()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年06期
页码:
122-128
栏目:
出版日期:
2024-09-25

文章信息/Info

Title:
Analysis and Evaluation Model of Smart Grid Operation State Basedon Graph Neural Network
文章编号:
1671-6833(2024)06-0122-07
作者:
刘慧林1;  范瑞明1;  程大闯2;  彭 珑1;  张国亮1;  张兆功3
1. 国网冀北电力有限公司电力科学研究院,北京 100032;2. 北京科东电力控制系统有限责任公司,北京 100089;3. 黑龙江大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001
Author(s):
LIU Huilin1;  FAN Ruiming1;  CHENG Dachuang2;  PENG Long1;  ZHANG Guoliang1;  ZHANG Zhaogong3
1. Electric Power Research Institute of State Grid Jibei Electric Power Co. , Ltd. , Beijing 100032, China; 2. Beijing Kedong Electric Power Control System Co. , Ltd. , Beijing 100089, China; 3. School of Computer Science and Technology, Heilongjiang University,Harbin 150001, China
关键词:
稳定性评估;  故障定位;  数据填补;  长短时记忆网络;  图神经网络
Keywords:
stability assessment;  fault location;  data filling;  long short-term memory network;  graph neural network
分类号:
TM712
DOI:
10.13705/j.issn.1671-6833.2024.06.017
文献标志码:
A
摘要:
智能电网的安全运行是保证持续、高效供电的首要前提,为此,提出了一种基于图神经网络( GNN) 的智能电网运行状态分析与评估模型。 首先,利用长短记忆网络对量测数据中存在的缺失数据进行填补,以确保模型用于稳定性评估和故障定位时具有良好的性能。 其次,基于 GNN 分别设计了用于电网运行稳定状态评估的二分类器和用于故障元件定位的多分类器。 所提模型能够充分挖掘电网运行数据的时空特性,与其他方法相比,所提模型在不同量测条件下均表现出更为优异的性能。 实验结果表明:当量测数据时长为 0. 1 s 时,所提模型稳定性评估和故障定位准确率分别为 0. 985 5 和 0. 981 4,高于其他模型;当仅可量测到一半元件数据时,所提模型稳定性评估、母线故障定位及发电机故障定位的准确率分别为 0. 998 0,0. 960 9 以及 0. 981 2,高于其他模型。
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.

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更新日期/Last Update: 2024-09-29