考虑配电网级联故障效应的时空深度网络智能预警方法

方正基; 潘凯岩; 孔明珠; 刘华; 赵洪睿; 刘宏达

方正基,潘凯岩,孔明珠,刘华,赵洪睿,刘宏达.考虑配电网级联故障效应的时空深度网络智能预警方法[J].电测与仪表,2025,62(11):210-215.
FANG Zhengji,PAN Kaiyan,KONG Minghzu,LIU Hua,ZHAO Hongrui,LIU Hongda.An intelligent early warning method for spatio-temporal deep network considering cascading fault effects in distribution networks[J].Electrical Measurement & Instrumentation,2025,62(11):210-215.

考虑配电网级联故障效应的时空深度网络智能预警方法

An intelligent early warning method for spatio-temporal deep network considering cascading fault effects in distribution networks

DOI：10.19753/j.issn1001-1390.2025.11.024

中文关键词: 级联故障深度网络配电网故障预警

英文关键词:cascading fault, deep network, distribution network, fault early warning

基金项目:南方电网科技项目，编号：030400KK52190115。

作者中文名	作者英文名	单位
方正基	FANG Zhengji	东方电子股份有限公司，山东烟台 264000
潘凯岩	PAN Kaiyan	1.东方电子股份有限公司，山东烟台 264000；2.哈尔滨工程大学智能科学与工程学院，哈尔滨 150000
孔明珠	KONG Minghzu	东方电子股份有限公司，山东烟台 264000
刘华	LIU Hua	东方电子股份有限公司，山东烟台 264000
赵洪睿	ZHAO Hongrui	东方电子股份有限公司，山东烟台 264000
刘宏达	LIU Hongda	哈尔滨工程大学智能科学与工程学院，哈尔滨 150000

摘要点击次数: 16

中文摘要:

级联故障由于其组件之间的依赖性易造成配电网系统面临不稳定危害的重大风险，并最终导致整个网络完全崩溃。考虑到尽早预警级联故障有利于促进自愈技术的实现，文章引入一种时空深度网络预测级联故障的响应时间。利用模型中的卷积操作同时结合上、下采样提取出配网系统网络拓扑及电气参量所呈现的空间和时域特性。响应时间按紧急程度划分为三个等级，将模型的预测任务制定为一个多分类问题。通过在UIUC-150节点电力系统上的实验结果表明，利用电力网络的初始状态和级联故障开始阶段元件的初始故障集，该模型能够获得相比其他传统方法更加准确的预测精度。这些结果揭示出级联故障传播的完整动态轮廓，并确定出每个级联故障场景的响应时间，为系统发生大面积级联故障预警提供了新的见解。

英文摘要:

Cascading faults are prone to cause significant risk of instability hazards to the distribution network system due to the dependency between its components, and eventually lead to the complete collapse of the whole network. Considering that early warning of cascading faults can facilitate the implementation of self-healing techniques, this paper introduces a spatio-temporal deep network to predict the response time of cascading faults. The spatial and time-domain characteristics presented by the network topology and electrical parameters of the distribution system are extracted using the convolution operation in the model combined with both up- and down-sampling. The response time is classified into three classes according to the degree of urgency, and the prediction task of the model is formulated as a multiclassification problem. Experimental results on the UIUC 150-bus power system show that the model is able to obtain more accurate prediction accuracy compared to other conventional methods by using the initial state of the power network and the initial set of faults of the elements at the beginning of the cascading fault. These results reveal the complete dynamic profile of cascading fault propagation and determine the response time for each cascading fault scenario, providing new insights for early warning of large cascading faults occurring in the system.

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