1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems
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DOI：
10.1109/oncon56984.2022.10126859
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发表时间：
2022-11
期刊：
影响因子：
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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
本文提出了一种用于微电网故障检测的一维卷积网络和图卷积网络。一维卷积神经网络(1D-CNN)和图卷积网络(GCN)的结合有助于从微电网电压测量中提取时空相关性。故障检测方案包括故障事件检测、故障类型和故障相分类、故障定位。有五种神经网络模型训练来处理这些任务。采用迁移学习和微调来减少培训工作量。在波茨坦13节点微电网数据集上，比较了一维卷积和图形卷积网络(1D-CGCN)和传统的ANN结构。故障事件检测、故障类型分类、故障相识别和故障定位的准确率分别达到99.5%、98.4%、99.2%和95.5%。给出了故障类型和故障相分类的详细混淆矩阵，以供验证。
This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.