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.

翻译：潮流分析在检查电力系统网络中的电能流动方面起着至关重要的作用。通过进行潮流计算，可以确定系统的稳态变量，包括电压幅值、各母线相位角以及支路有功/无功功率流。虽然广泛使用的直流潮流模型具有快速和鲁棒性的优点，但对于某些输电线路，其给出的线路潮流结果可能不够准确。当涉及风电场等可再生能源（通常远离主电网）时，这一问题变得更加关键。获取这些关键线路的精确潮流结果对于后续运行至关重要。为应对这些挑战，数据驱动方法利用历史电网数据进行研究。本文使用历史电力系统数据训练了一个图神经网络模型，用于预测潮流结果。该图神经网络模型能够快速估计线路潮流。通过全面性能分析，将所提出的基于图神经网络的潮流模型与传统直流潮流模型、深度神经网络和卷积神经网络进行了比较。在测试系统上的结果表明，与基准模型相比，所提出的基于图神经网络的潮流模型能够以高效率提供更精确的解。