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Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

Hoffmann, M. W.; Wildermuth, S.; Gitzel, R.; Boyaci, A.; Gebhardt, J.; Kaul, H.; Amihai, I.; Forg, B.; Suriyah, M.; Leibfried, T.; Stich, V.; Hicking, J.; Bremer, M.; Kaminski, L.; Beverungen, D.; Zur Heiden, P.; Tornede, T.

Abstract:
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.

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Verlagsausgabe §
DOI: 10.5445/IR/1000118965
Veröffentlicht am 15.05.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Elektroenergiesysteme und Hochspannungstechnik (IEH)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000118965
Erschienen in Sensors
Band 20
Heft 7
Seiten Article: 2099
Schlagwörter energy revolution; condition monitoring; switchgear; infrared sensor; predictive maintenance; machine learning; thermal monitoring; business model
Nachgewiesen in Scopus
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