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

TL;DR

This paper demonstrates how unsupervised clustering, specifically K-Means, can effectively analyze unlabeled voltage and current waveform data to identify and categorize faults in high-voltage power systems, aiding scalable fault diagnosis.

Contribution

It introduces an unsupervised fault analysis method using FFT features and K-Means clustering, reducing reliance on labeled datasets for power system fault classification.

Findings

Clusters align with real fault characteristics

Unsupervised approach enables scalable fault analysis

Potential for automated fault detection in power grids

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 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.