Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
This work addresses fault diagnosis challenges for power grid operators, but it is incremental as it applies existing methods to a new dataset.
This paper tackled the problem of fault classification in high-voltage power systems without labeled data by applying unsupervised clustering to voltage and current signals, demonstrating its potential for scalable fault analysis through expert evaluation.
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.