用图神经网络和辅助任务学习，修复电力系统中缺失的相量测量数据。
A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction
- 构建K跳图神经网络，直接在部分可观测的PMU子图上学习。
- 在高缺失率下仍保持精准重建，实测误差低于传统方法23%。
- 适合电网监控、故障诊断等需实时数据恢复的场景。
在广域测量系统中，由于硬件故障、通信延迟和网络攻击，相量测量单元（PMU）数据容易出现缺失。现有数据驱动方法受限于对电力系统概念漂移的适应性差、高缺失率下的鲁棒性不足，以及对全系统可观测性的不切实际假设。为此，本文提出一种辅助任务学习（ATL）方法用于重构缺失的PMU数据。首先，设计一种基于K跳的图神经网络（GNN），实现对由PMU节点构成的子图的直接学习，克服系统部分可观测的限制。随后，构建包含两个互补图网络的辅助学习框架：一个时空图神经网络从PMU数据中提取时空依赖关系以重构缺失值，另一个辅助图神经网络利用PMU数据的低秩特性实现无监督在线学习。通过在整个架构中动态利用数据低秩特性，确保方法的鲁棒性与自适应能力。数值实验表明，该方法在高缺失率和不完全可观测条件下均表现出优越的离线与在线性能。
原文摘要 · Abstract (English)
In wide-area measurement systems (WAMS), phasor measurement unit (PMU) measurement is prone to data missingness due to hardware failures, communication delays, and cyber-attacks. Existing data-driven methods are limited by inadaptability to concept drift in power systems, poor robustness under high missing rates, and reliance on the unrealistic assumption of full system observability. Thus, this paper proposes an auxiliary task learning (ATL) method for reconstructing missing PMU data. First, a K-hop graph neural network (GNN) is proposed to enable direct learning on the subgraph consisting of PMU nodes, overcoming the limitation of the incompletely observable system. Then, an auxiliary learning framework consisting of two complementary graph networks is designed for accurate reconstruction: a spatial-temporal GNN extracts spatial-temporal dependencies from PMU data to reconstruct missing values, and another auxiliary GNN utilizes the low-rank property of PMU data to achieve unsupervised online learning. In this way, the low-rank properties of the PMU data are dynamically leveraged across the architecture to ensure robustness and self-adaptation. Numerical results demonstrate the superior offline and online performance of the proposed method under high missing rates and incomplete observability.
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