用变分图自编码器判断电力系统潮流解是否可行。

Power Flow Feasibility Assessment Using Variational Graph Autoencoders

• 构建变分图自编码器，从电网拓扑中学习潮流解的可行性特征。
• 在IEEE 118节点系统上实现98.7%的可行性检测准确率。
• 适合需要验证AI电力计算结果可靠性的研究人员和工程师。

近年来，数据驱动方法（包括图神经网络）被用于加速潮流计算，但对解的可行性关注极少，而传统求解器可提供该信息。本文提出一种变分图自编码器（VGAE），利用IEEE 118节点系统评估由人工智能驱动求解器提供的解的有效性，实现对潮流解可行性进行自动检测。模型通过学习电网图结构与运行状态的联合分布，识别出不可行解的关键特征。实验表明，在测试集上该方法对不可行解的检测准确率达98.7%，显著提升电力系统中基于机器学习方法的可靠性。该框架可作为现有智能求解器的后处理验证模块，增强其在实际应用中的可信度。

原文摘要 · Abstract (English)

Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers. This paper presents a Variational Graph Autoencoder (VGAE) that detects the power flow solution feasibility, using the IEEE 118-bus case, to assess the validity of the solutions provided by AI-driven solvers.

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