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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

1
ABB AG, Corporate Research Germany, 68526 Ladenburg, Germany
2
Heimann Sensor GmbH, 01109 Dresden, Germany
3
Institute of Electric Energy Systems and High Voltage Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
4
FIR (Institute for Industrial Management) at the RWTH Aachen University, 52074Aachen, Germany
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Chair of Business Information Systems, Paderborn University, 33098 Paderborn, Germany
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Software Innovation Campus Paderborn, Department of Computer Science and Heinz Nixdorf Institute, Paderborn University, 33098 Paderborn, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2099; https://doi.org/10.3390/s20072099
Received: 20 February 2020 / Revised: 2 April 2020 / Accepted: 3 April 2020 / Published: 8 April 2020
(This article belongs to the Special Issue Sensors for Societal Automation)
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. View Full-Text
Keywords: energy revolution; condition monitoring; switchgear; infrared sensor; predictive maintenance; machine learning; thermal monitoring; business model energy revolution; condition monitoring; switchgear; infrared sensor; predictive maintenance; machine learning; thermal monitoring; business model
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MDPI and ACS Style

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. Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions. Sensors 2020, 20, 2099. https://doi.org/10.3390/s20072099

AMA Style

Hoffmann MW, 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. Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions. Sensors. 2020; 20(7):2099. https://doi.org/10.3390/s20072099

Chicago/Turabian Style

Hoffmann, Martin W.; Wildermuth, Stephan; Gitzel, Ralf; Boyaci, Aydin; Gebhardt, Jörg; Kaul, Holger; Amihai, Ido; Forg, Bodo; Suriyah, Michael; Leibfried, Thomas; Stich, Volker; Hicking, Jan; Bremer, Martin; Kaminski, Lars; Beverungen, Daniel; zur Heiden, Philipp; Tornede, Tanja. 2020. "Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions" Sensors 20, no. 7: 2099. https://doi.org/10.3390/s20072099

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