Publications Details

A Machine Learning-based Method using the Dynamic Mode Decomposition for Fault Location and Classification

Wilches-Bernal, Felipe; Jimenez Aparicio, Miguel J.; Reno, Matthew J.

A novel method for fault classification and location is presented in this paper. This method is divided into an initial signal processing stage that is followed by a machine learning stage. The initial stage analyzes voltages and currents with a window-based approach based on the dynamic mode decomposition (DMD) and then applies signal norms to the resulting DMD data. The outputs for the signal norms are used as features for a random-forests for classifying the type of fault in the system as well as for fault location purposes. The method was tested on a small distribution system where it showed an accuracy of 100% in fault classification and a mean error of ~ 30 m when predicting the fault location.