Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals

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 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 Réseau de Transport d'Électricité (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 by structuring large unlabeled waveform datasets and supporting targeted expert review.

13 pages, 9 figures, 6 tables. Presented at the 27th Annual Georgia Tech Fault and Disturbance Analysis Conference, May 5-6, 2025. Revised clustering analysis and evaluation for internal consistency

Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2505.17763 [cs.LG]

(or arXiv:2505.17763v2 [cs.LG] for this version)