论文标题
在存在拓扑变化的情况下，使用图神经网络的时间同步状态估计
Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
论文作者
论文摘要
最近，主要重点是开发涉及机器学习（ML）的数据驱动方法（用于电力系统中的高速静态状态估计（SE））。重点源于ML克服与基于模型的方法相关的困难的能力，例如处理非高斯测量噪声。但是，拓扑变化对执行基于ML的SE构成了艰巨的挑战，因为当发生这种变化时，训练和测试环境会不同。本文通过制定基于图形神经网络（GNN）的时间同步状态估计器来避免这一挑战，该估计值考虑训练本身在训练过程中考虑了功率系统的物理连接。使用IEEE 118-BUS系统获得的结果表明，基于GNN的状态估计器在存在非高斯测量噪声和拓扑变化的情况下，基于模型的线性状态估计器和基于数据驱动的深神经网络状态估计器的表现均优于基于模型的线性状态估计器。
Recently, there has been a major emphasis on developing data-driven approaches involving machine learning (ML) for high-speed static state estimation (SE) in power systems. The emphasis stems from the ability of ML to overcome difficulties associated with model-based approaches, such as handling of non-Gaussian measurement noise. However, topology changes pose a stiff challenge for performing ML-based SE because the training and test environments become different when such changes occur. This paper circumvents this challenge by formulating a graph neural network (GNN)-based time-synchronized state estimator that considers the physical connections of the power system during the training itself. The results obtained using the IEEE 118-bus system indicate that the GNN-based state estimator outperforms both the model-based linear state estimator and a data-driven deep neural network-based state estimator in the presence of non-Gaussian measurement noise and topology changes, respectively.
