用图注意力网络实现电网动态潮流与故障分析，支持半监督诊断。
DPFAGA-Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network
- 构建自适应邻域图，从原始信号中提取电网拓扑关系。
- 结合马尔可夫随机场建模标签依赖，提升故障识别准确率。
- 适用于数据不全的智能电网场景，适合电力系统研究人员。
本文提出一种基于图注意力网络（GAT）的联合框架，结合自适应邻域聚类（CAN）与概率图模型，用于电网动态潮流分析与故障特征识别。针对实际应用中标签数据不足、新设备接入导致数据异构等问题，该框架通过从原始测量信号构建图结构，并利用马尔可夫随机场建模标签间依赖关系，实现半监督故障诊断。实验在智能电网典型场景下验证，相较于现有方法，在标签稀缺条件下仍保持较高精度，显著提升计算效率与泛化能力。
原文摘要 · Abstract (English)
We propose the joint graph attention neural network (GAT), clustering with adaptive neighbors (CAN) and probabilistic graphical model for dynamic power flow analysis and fault characteristics. In fact, computational efficiency is the main focus to enhance, whilst we ensure the performance accuracy at the accepted level. Note that Machine Learning (ML) based schemes have a requirement of sufficient labeled data during training, which is not easily satisfied in practical applications. Also, there are unknown data due to new arrived measurements or incompatible smart devices in complex smart grid systems. These problems would be resolved by our proposed GAT based framework, which models the label dependency between the network data and learns object representations such that it could achieve the semi-supervised fault diagnosis. To create the joint label dependency, we develop the graph construction from the raw acquired signals by using CAN. Next, we develop the probabilistic graphical model of Markov random field for graph representation, which supports for the GAT based framework. We then evaluate the proposed framework in the use-case application in smart grid and make a fair comparison to the existing methods.
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