Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems
Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems
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DOI：
10.1109/access.2023.3273292
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发表时间：
2022-10
期刊：
影响因子：
3.9
通讯作者：
Bang L. H. Nguyen;T. Vu;Thai-Thanh Nguyen;M. Panwar;R. Hovsapian
中科院分区：
文献类型：
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作者：
Bang L. H. Nguyen;T. Vu;Thai-Thanh Nguyen;M. Panwar;R. Hovsapian
故障诊断对于决定采取适当的措施进行故障隔离和系统恢复非常重要。基于逆变器的分布式能源的日益集成对使用传统的过流继电器进行故障检测产生了巨大的影响。本文利用新兴的图学习技术，建立了一种新的故障诊断时态递归图神经网络模型。时间递归图神经网络结构可以从安装在关键节点上的电压测量单元的数据中提取时空特征。根据这些特征，执行故障事件检测、故障类型/相分类和故障定位。与前人的工作相比，所提出的时间递归图神经网络为故障诊断提供了更好的泛化。此外，该方案提取的是电压信号，而不是电流信号，因此不需要在配电系统的所有线路上安装继电器。因此，该方案具有较好的通用性，不受继电保护安装数量的限制。在波茨坦微电网和IEEE123节点系统上与其他神经网络结构进行了比较，综合评价了该方法的有效性。
Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.