Hierarchical Agglomerative Clustering of Short-Circuit Faults in Transmission Lines

Data mining can play a fundamental role in modern power systems. However, a major problem is to extract useful information from the currently available non-labeled digitized time series. This work proposes a new methodology based on hierarchical clustering for labeling faults that occurred in transmission lines. A graphical user interface can benefit from the complementary information provided by the methodology. These faults are responsible for the majority of the disturbances and cascading blackouts. Simulation results using the public dataset UFPA faults are presented to validate the proposed method.