TY  - JOUR
A1  - Hoffmann, Martin W.
A1  - Wildermuth, Stephan
A1  - Gitzel, Ralf
A1  - Boyaci, Aydin
A1  - Gebhardt, Jörg
A1  - Kaul, Holger
A1  - Amihai, Ido
A1  - Forg, Bodo
A1  - Suriyah, Michael
A1  - Liebfried, Thomas
A1  - Stich, Volker
A1  - Hicking, Jan
A1  - Bremer, Martin
A1  - Kaminski, Lars
A1  - Beverungen, Daniel
A1  - zur Heiden, Philipp
A1  - Tornede, Tanja
T1  - Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions
T2  - Sensors Journal
N2  - The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.
KW  - business model
KW  - infrared sensor
KW  - energy revolution
KW  - condition monitoring
KW  - thermal monitoring
KW  - machine learning
KW  - switchgear
KW  - Predictive Maintenance
Y1  - 2020
UR  - https://epub.fir.de/frontdoor/index/index/docId/226
UR  - https://www.semanticscholar.org/paper/Integration-of-Novel-Sensors-and-Machine-Learning-Hoffmann-Wildermuth/9e62a52d95eee41ea479e33af9dbc52177bf43d9
SN  - 1424-8220
VL  - 20
IS  - 9
ER  -