Name
Title | ||
|---|---|---|
A Machine Learning-based Method using the Dynamic Mode Decomposition for Fault Location and Classification |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ISGT50606.2022.9817543 | 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) |
Keywords | DocType | ISSN |
Dynamic Mode Decomposition,Fault Location,Fault Classification,Random Forests,Machine Learning | Conference | 2167-9665 |
ISBN | Citations | PageRank |
978-1-6654-3776-9 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felipe Wilches-Bernal | 1 | 0 | 1.35 |
| Miguel Jiménez-Aparicio | 2 | 0 | 0.68 |
| Matthew J. Reno | 3 | 0 | 0.68 |