用无监督聚类分析电压电流数据，自动识别高压电网故障类型。
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
- 通过FFT提取频域特征，用K-Means聚类无标签数据。
- 聚类结果与专家判断一致，可自动分类故障类型。
- 适合缺乏标注数据的电力系统故障诊断场景。
现代电网中传感器广泛应用，积累了大量电压与电流波形数据，尤其在故障发生时。然而，缺乏标注数据给故障分类带来挑战。本文研究了无监督聚类技术在高压电网故障诊断中的应用。基于法国输电公司（RTE）提供的数据集，采用快速傅里叶变换（FFT）提取频域特征，再使用K-Means算法识别数据中的潜在模式，实现无需标注样本的自动化故障分类。聚类结果经电力系统专家评估，确认其与实际故障特征高度吻合。实验表明，该方法在无先验假设下具备可扩展性与数据驱动能力，为故障检测与分类提供可靠方案。
原文摘要 · Abstract (English)
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 challenge for fault classification and analysis. This paper explores the application of unsupervised clustering techniques for fault diagnosis in high-voltage power systems. A dataset provided by the Reseau de Transport d'Electricite (RTE) is analyzed, with frequency domain features extracted using the Fast Fourier Transform (FFT). The K-Means algorithm is then applied to identify underlying patterns in the data, enabling automated fault categorization without the need for labeled training samples. The resulting clusters are evaluated in collaboration with power system experts to assess their alignment with real-world fault characteristics. The results demonstrate the potential of unsupervised learning for scalable and data-driven fault analysis, providing a robust approach to detecting and classifying power system faults with minimal prior assumptions.
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