English(EN) Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals

无监督聚类方法有助于电力系统故障分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种无监督聚类方法，利用电压和电流信号分析高压电力系统中的故障事件。该方法利用了 Réseau de Transport d'Électricité (RTE) 的数据，并通过快速傅里叶变换 (FFT) 提取频域特征。然后应用 K-Means 算法对故障进行分类，而无需标记数据，聚类结果由电力系统专家进行验证。 AI

影响这项研究展示了无监督学习在关键基础设施中进行可扩展和数据驱动的故障分析的潜力。

排序理由该集群包含一篇学术论文，详细介绍了电力系统故障分析的新方法。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv cs.LG TIER_1 English(EN) · Julian Oelhaf, Georg Kordowich, Andreas Maier, Johann J\"ager, Siming Bayer · 2026-09-15 04:00

使用电压和电流信号对高压电力系统故障进行无监督聚类分析

arXiv:2505.17763v2 Announce Type: replace Abstract: The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially during fault events. However, the lack of labeled datasets poses a significant cha…

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使用电压和电流信号对高压电力系统故障进行无监督聚类分析

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