| 引用本文: | 张宸滔,郑永康,卢继平,等.基于图神经网络的智能变电站二次回路故障定位研究[J].电力系统保护与控制,2022,50(11):81-90.[点击复制] |
| ZHANG Chentao,ZHENG Yongkang,LU Jiping,et al.Fault location of secondary circuits in a smart substation based on a graph neural network[J].Power System Protection and Control,2022,50(11):81-90[点击复制] |
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| 摘要: |
| 为提高智能变电站二次回路故障定位的准确率与可移植性，提出了一种基于图神经网络的智能变电站二次回路故障定位方法。依据智能变电站配置文件制作图数据库，得到二次设备之间的连接关系。结合二次设备告警信号表征，提出了一种二次设备节点的信息表征作为图神经网络的输入。利用图神经网络的理论建立了故障定位模型。以某220 kV智能变电站的二次回路为基准，利用故障图生成模型改变组网方式、订阅关系及网络配置产生不同二次回路故障构成算例。通过实验比较了所提定位方法与其他模型的准确率，实验结果表明该方法有较高的定位精度及较好的鲁棒性。 |
| 关键词: 图神经网络 智能变电站 二次回路 故障定位 |
| DOI：DOI: 10.19783/j.cnki.pspc.211042 |
| 投稿时间：2021-08-07修订日期：2021-09-24 |
| 基金项目:国家电网公司科技项目资助(5108-202018037A- 0-0-00)；国网四川省电力公司科技项目资助(521997190004) |
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| Fault location of secondary circuits in a smart substation based on a graph neural network |
| ZHANG Chentao,ZHENG Yongkang,LU Jiping,ZHANG Hao,REN Hui,YANG Zhi |
| (1. State Grid Sichuan Electric Power Co., Ltd. Research Institute, Chengdu 610041, China; 2. State Key Laboratory of Power
Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, China;
3. Maintenance Branch, State Grid Sichuan Electric Power Co., Ltd., Chengdu 610042, China;
4. Nanjing Branch, China Electric Power Research Institute Co., Ltd., Nanjing 210003, China) |
| Abstract: |
| To improve the accuracy and portability of secondary circuit fault location in smart substations, a graph neural network-based smart substation secondary circuit fault location method is proposed. From the substation configuration description file, a graph database is established to obtain the connection relationship between the secondary equipment. Combined with the alarm signal representation of the secondary equipment, a kind of information representation of the secondary equipment node is proposed as the input of the graph neural network. Graph neural network theory is used to establish the fault location model. Taking the secondary circuits of a 220 kV smart substation as a benchmark, using the fault generation model to change the networking mode, a subscription relationship and network configuration is used to generate different secondary circuits faults. The experiment compares the accuracy of the proposed fault location model with other models and the experimental results show that this method has higher fault location accuracy and better robustness.
This work is supported by the Science and Technology Project of State Grid Corporation of China (No. 5108-202018037A-0-0-00). |
| Key words: graph neural network intelligent substation secondary circuits fault location |
