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arXiv 2602.19667eess.SYcs.SY
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机器学习负荷流代理中训练数据集大小的影响

Impact of Training Dataset Size for ML Load Flow Surrogates

Friedrich-Alexander-Universität Erlangen-Nürnberg(埃尔朗根-纽伦堡大学)

Timon Conrad, Changhun Kim, Johann Jäger, Andreas Maier, Siming Bayer

AI总结:

本文研究了机器学习在负荷流代理中训练数据集大小的影响,发现图神经网络在样本效率上表现最佳,但大训练数据集仍是影响性能的关键因素。

摘要:

高效的准确负荷流计算是现代电力系统运行的基础。经典数值方法如牛顿-拉夫森算法提供高度精确的结果,但计算需求高,限制了其在大规模情景研究和时间敏感优化中的应用。研究表明,机器学习方法可以高精度近似负荷流结果,同时显著减少计算时间。样本效率,即在有限训练数据集下实现高精度的能力,仍研究不足,特别是在具有固定拓扑结构的电网中。本文系统研究了多层感知机和两种图神经网络变体在基于修改后的IEEE 5节点系统的数据集上的样本效率。对于该电网规模的结果显示,图神经网络实现了最低的损失。然而,大型训练数据集的可用性仍然是影响性能的主要因素,相比架构选择。

英文摘要:

Efficient and accurate load flow calculations are a bedrock of modern power system operation. Classical numerical methods such as the Newton-Raphson algorithm provide highly precise results but are computationally demanding, which limits their applicability in large-scale scenario studies and optimization in time-critical contexts. Research has shown that machine learning approaches can approximate load flow results with high accuracy while substantially reducing computation time. Sample efficiency, i.e., the ability to achieve high accuracy with limited training dataset size, is still insufficiently researched, especially in grids with a fixed topology. This paper presents a systematic investigation of the sample efficiency of a Multilayer Perceptron and two Graph Neural Network variants on a dataset based on a modified IEEE 5-bus system. The results for this grid size show that Graph Neural Networks achieve the lowest losses. However, the availability of large training datasets remains the dominant factor influencing performance compared to architecture choice.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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