Accurate and efficient power flow (PF) analysis is crucial in modern
electrical networks' efficient operation and planning. Therefore, there is a
need for scalable algorithms capable of handling large-scale power networks
that can provide accurate and fast solutions. Graph Neural Networks (GNNs) have
emerged as a promising approach for enhancing the speed of PF approximations by
leveraging their ability to capture distinctive features from the underlying
power network graph. In this study, we introduce PowerFlowNet, a novel GNN
architecture for PF approximation that showcases similar performance with the
traditional Newton-Raphson method but achieves it 4 times faster in the simple
IEEE 14-bus system and 145 times faster in the realistic case of the French
high voltage network (6470rte). Meanwhile, it significantly outperforms other
traditional approximation methods, such as the DC relaxation method, in terms
of performance and execution time; therefore, making PowerFlowNet a highly
promising solution for real-world PF analysis. Furthermore, we verify the
efficacy of our approach by conducting an in-depth experimental evaluation,
thoroughly examining the performance, scalability, interpretability, and
architectural dependability of PowerFlowNet. The evaluation provides insights
into the behavior and potential applications of GNNs in power system analysis.

PowerFlowNet 是一种使用图神经网络的电流流估计方法，在简单的 IEEE 14 总线系统中比传统的 Newton-Raphson 方法更快 4 倍，在法国高压网络中快 145 倍，且在性能和执行时间方面明显优于其他传统估计方法，其在实际电流流分析中表现出巨大潜力，通过深入的实验评估验证了该方法的功效、可扩展性、可解释性和架构可靠性，为电力系统分析中图神经网络的行为和潜在应用提供了深刻见解。

PowerFlowNet：利用消息传递 GNNs 提高功率流近似

PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow  Approximation

This paper explores the potential application of quantum and hybrid
quantum-classical neural networks in power flow analysis. Experiments are
conducted using two small-size datasets based on the IEEE 4-bus and 33-bus test
systems. A systematic performance comparison is also conducted among quantum,
hybrid quantum-classical, and classical neural networks. The comparison is
based on (i) generalization ability, (ii) robustness, (iii) training dataset
size needed, (iv) training error. (v) training computational time, and (vi)
training process stability. The results show that the developed
quantum-classical neural network outperforms both quantum and classical neural
networks, and hence can improve deep learning-based power flow analysis in the
noisy-intermediate-scale quantum (NISQ) era.

探索量子和混合量子 - 经典神经网络在电力流分析中的潜在应用。使用基于 IEEE 4-bus 和 33-bus 测试系统的两个小型数据集进行实验，并对量子、混合量子 - 经典和经典神经网络进行系统性能比较。结果表明，开发的混合量子 - 经典神经网络优于量子和经典神经网络，因此可以改善基于深度学习的电力流分析在噪声中等规模的量子 (NISQ) 时代。

量子神经网络用于电力流分析

Quantum Neural Networks for Power Flow Analysis

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。