基于深度学习的智能变电站通信链路故障定位方法

doi:10.11930/j.issn.1004-9649.202210085

摘要/Abstract

摘要： 针对智能变电站通信链路故障定位因链路复杂导致排查效率低的问题，提出了基于深度学习的智能变电站通信链路故障定位方法。从智能变电站二次装置网络拓扑出发，构建网络连通矩阵并作为基准，提出了通信链路故障情形下的故障特征表征方法；进一步基于二次装置连接与运行状态之间的逻辑关系，构建全站故障样本集；应用改进卷积神经网络（CNN），搭建智能变电站通信链路故障定位模型，最终通过后台信息初步判定的故障间隔信息与模型输出结果共同实现故障链路精确定位。以220 kV智能变电站部分间隔为例，构建故障样本集，通过结果分析对比了不同定位方法，对比结果表明所提定位方法具有较高的准确率。

关键词: 智能变电站, 通信链路, 卷积神经网络, 故障定位

Abstract: Aiming at the problem of low troubleshooting efficiency of communication link faults caused by complex links in smart substation, a deep learning based fault location method for intelligent substation communication link of smart substation is proposed. Firstly, based on the network topology of secondary devices in smart substations, a network connectivity matrix is constructed and used as the benchmark, and a fault feature characterization method is proposed for communication link faults. And then, based on the logical relationship between secondary device connection and operation status, a fault sample set of the whole station is constructed. The improved CNN is applied to build the fault location model of the smart substation communication link. Finally, the fault link is accurately located through the fault bay information preliminarily determined by the background information and the model output results. A 220 kV smart substation is taken for case study and some bays of it are taken to construct the fault sample set, and different fault location methods are compared through result analysis. The comparison results show that the proposed location method has higher accuracy.

Key words: smart substation, communication link, CNN, fault location

皮志勇, 朱益, 廖玄, 李振兴, 方豪, 吴沛. 基于深度学习的智能变电站通信链路故障定位方法[J]. 中国电力, 2023, 56(7): 136-145.

PI Zhiyong, ZHU Yi, LIAO Xuan, LI Zhenxing, FANG Hao, WU Pei. Fault Location Method for Communication Link in Smart Substation Based on Deep Learning[J]. Electric Power, 2023, 56(7): 136-145.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202210085

https://www.electricpower.com.cn/CN/Y2023/V56/I7/136

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