用变分图自编码器生成电力配电网数据，验证其效果与局限。
Exploring Variational Graph Autoencoders for Distribution Grid Data Generation
- 采用四种解码器构建变分图自编码器，生成电网拓扑结构。
- GCN解码器在简单数据集上表现良好，复杂数据集出现断连和重复结构。
- 开源模型与评估工具，助力电力系统机器学习研究。
为解决能源网络机器学习研究中公开电力系统数据不足的问题，本文探究了变分图自编码器（VGAEs）在合成配电网生成中的应用。基于两个开源数据集ENGAGE和DINGO，评估了四种解码器变体，并通过结构与谱性指标对比生成网络与原始电网。结果表明，简单解码器无法捕捉真实拓扑，而基于GCN的方案在ENGAGE上表现良好，但在更复杂的DINGO数据集上产生断连组件和重复模式等伪影。研究揭示了VGAE在电网合成中的潜力与局限，强调需要更强大的生成模型与稳健评估方法。相关模型与分析代码已开源，以支持基准测试并加速机器学习驱动的电力系统研究。
原文摘要 · Abstract (English)
To address the lack of public power system data for machine learning research in energy networks, we investigate the use of variational graph autoencoders (VGAEs) for synthetic distribution grid generation. Using two open-source datasets, ENGAGE and DINGO, we evaluate four decoder variants and compare generated networks against the original grids using structural and spectral metrics. Results indicate that simple decoders fail to capture realistic topologies, while GCN-based approaches achieve strong fidelity on ENGAGE but struggle on the more complex DINGO dataset, producing artifacts such as disconnected components and repeated motifs. These findings highlight both the promise and limitations of VGAEs for grid synthesis, underscoring the need for more expressive generative models and robust evaluation. We release our models and analysis as open source to support benchmarking and accelerate progress in ML-driven power system research.
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