标题： 基于拓扑感知图神经网络的状态估计算法用于PMU不可观测的电力系统 显示英文标题

标题： Topology-Aware Graph Neural Network-based State Estimation for PMU-Unobservable Power Systems

摘要： 基于传统优化的时间同步状态估计（SE）技术通常面临高在线计算负担、相量测量单元（PMU）覆盖范围有限以及非高斯测量噪声等问题。尽管已经开发出基于传统学习的模型来克服这些挑战，但它们会受到拓扑变化和实时数据丢失的负面影响。本文提出了一种基于图神经网络（GNN）的新颖深度几何学习方法，用于估计PMU不可观测电力系统的状态。所提出的方案在一个定制的端到端学习框架内结合了图卷积和多头图注意力层，以处理拓扑变化和实时数据丢失问题。还推导出了一个关于拓扑变化的SE误差上界。针对不同测试系统的实验结果表明，在存在拓扑变化、PMU故障、坏数据、非高斯测量噪声和大规模系统实现的情况下，所提出的自定义GNN-SE（CGNN-SE）优于传统的基于优化的技术和常规的基于学习的模型。 显示英文摘要

摘要： Traditional optimization-based techniques for time-synchronized state estimation (SE) often suffer from high online computational burden, limited phasor measurement unit (PMU) coverage, and presence of non-Gaussian measurement noise. Although conventional learning-based models have been developed to overcome these challenges, they are negatively impacted by topology changes and real-time data loss. This paper proposes a novel deep geometric learning approach based on graph neural networks (GNNs) to estimate the states of PMU-unobservable power systems. The proposed approach combines graph convolution and multi-head graph attention layers inside a customized end-to-end learning framework to handle topology changes and real-time data loss. An upper bound on SE error as a function of topology change is also derived. Experimental results for different test systems demonstrate superiority of the proposed customized GNN-SE (CGNN-SE) over traditional optimization-based techniques as well as conventional learning-based models in presence of topology changes, PMU failures, bad data, non-Gaussian measurement noise, and large system implementation.