Non-Recursive System Identification and Fault Detection in LVDC and ELVDC Grids
Conference contribution
(Conference Contribution)
Publication Details
Author(s): Strobl C, Schäfer M, Rabenstein R
Publication year: 2018
Language: English
Abstract
Low end extra low voltage direct current grids
require selective fault protection designed for the specific application
and system voltage. System identification and machine
learning methods are helpful to identify, to localize and to classify
occurring fault events. A category of non-recursive large-signal
methods in the time domain for system identification and for
refined fault detection and analysis is introduced.
FAU Authors / FAU Editors
| Rabenstein, Rudolf Prof. Dr. |
| | |
| | | Lehrstuhl für Multimediakommunikation und Signalverarbeitung |
|
External institutions with authors
→ E-T-A Elektrotechnische Apparate GmbH |
How to cite
APA: | Strobl, C., Schäfer, M., & Rabenstein, R. (2018). Non-Recursive System Identification and Fault Detection in LVDC and ELVDC Grids. In Proceedings of the IEEE Int. Symp. on Circuits and Systems (ISCAS). Florence, IT. |
MLA: | Strobl, Christian, Maximilian Schäfer, and Rudolf Rabenstein. "Non-Recursive System Identification and Fault Detection in LVDC and ELVDC Grids." Proceedings of the IEEE Int. Symp. on Circuits and Systems (ISCAS), Florence 2018. |