Power flow analysis plays a crucial role in examining the electricity flow
within a power system network. By performing power flow calculations, the
system's steady-state variables, including voltage magnitude, phase angle at
each bus, active/reactive power flow across branches, can be determined. While
the widely used DC power flow model offers speed and robustness, it may yield
inaccurate line flow results for certain transmission lines. This issue becomes
more critical when dealing with renewable energy sources such as wind farms,
which are often located far from the main grid. Obtaining precise line flow
results for these critical lines is vital for next operations. To address these
challenges, data-driven approaches leverage historical grid profiles. In this
paper, a graph neural network (GNN) model is trained using historical power
system data to predict power flow outcomes. The GNN model enables rapid
estimation of line flows. A comprehensive performance analysis is conducted,
comparing the proposed GNN-based power flow model with the traditional DC power
flow model, as well as deep neural network (DNN) and convolutional neural
network (CNN). The results on test systems demonstrate that the proposed
GNN-based power flow model provides more accurate solutions with high
efficiency comparing to benchmark models.

本论文研究了使用图神经网络方法对历史电力系统数据进行训练，以预测电量流计算结果。通过在测试系统上的结果表明，使用所提出的图神经网络电量流模型可以比传统的直流（DC）功量流方法和深度神经网络（DNN）模型以及卷积神经网络（CNN）模型得到更准确有效的数据处理方案。