全球能源互联网2024，Vol.7Issue(3)：303-311,9.DOI:10.19705/j.cnki.issn2096-5125.2024.03.007
基于图神经网络的变压器短路电流计算方法
A Graph Neural Network-based Method for Transformer Short-circuit Current Calculation
邹德旭 1洪志湖 1代维菊 1黎文浩 2徐衍会 3郑乐3
作者信息
- 1. 南方电网云南电网有限责任公司电力科学研究院,云南省 昆明市 650217
- 2. 南方电网科学研究院有限责任公司,广东省 广州市 510700
- 3. 华北电力大学电气与电子工程学院,北京市 海淀区 100226
- 折叠
摘要
Abstract
With the increasing complexity of power systems in recent years,transformer operation safety has become a key issue that affects the stable operation of the power system.Currently,calculation methods of short-circuit current through transformers are mostly based on the power grid topology and transformer equivalent impedance.These methods have low flexibility and real-time performance and do not consider the actual operation mode of the system,making it difficult to meet the requirements of real-time operation of power systems.In this paper,a transformer short-circuit current calculation method considering flow conditions is proposed based on a graph convolutional neural network.By introducing the features of transformer bus and regional topology,a transformer short-circuit current calculation model is trained.This method introduces attention mechanisms to make the model more sensitive to dynamic power flow conditions under different operating conditions.Verified by an actual power grid example in a region,the calculated short-circuit current using this method has a small error compared to the reference value,and the distribution of calculation errors is concentrated,which can basically meet the requirements of short-circuit current calculation in practical operation.关键词
短路电流/变压器/图卷积神经网络/注意力机制Key words
short-circuit current/transformer/graph convolutional neural networks/attention mechanism分类
信息技术与安全科学引用本文复制引用
邹德旭,洪志湖,代维菊,黎文浩,徐衍会,郑乐..基于图神经网络的变压器短路电流计算方法[J].全球能源互联网,2024,7(3):303-311,9.
