Non-Recursive System Identification and Fault Detection in LVDC and ELVDC Grids

Strobl C, Schäfer M, Rabenstein R (2018)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2018

Event location: Florence IT

DOI: 10.1109/iscas.2018.8351714

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.

Authors with CRIS profile

Maximilian Schäfer Lehrstuhl für Multimediakommunikation und Signalverarbeitung (LMS) Rudolf Rabenstein Technische Fakultät

Involved external institutions

E-T-A Elektrotechnische Apparate GmbH DE Germany (DE)

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.

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