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

authored by: Martin W Hoffmann, Stephan Wildermuth, Ralf Gitzel, Aydin Boyaci, Joerg Gebhardt, Holger Kaul, Ido Amihai, Bodo Forg, Michael Suriyah, Thomas Leibfried, Volker Stich, Jan Hicking, Martin Bremer, Lars Kaminski, Daniel Beverungen, Philipp zur Heiden, Tanja Tornede
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.
External Organisation(s): Paderborn University
ABB AG -Transformatoren
Heimann Sensor GmbH
Karlsruhe Institute of Technology (KIT)
RWTH Aachen University
Type: Article
Journal: Sensors
Volume: 20
ISSN: 1424-3210
Publication date: 08.04.2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Analytical Chemistry, Information Systems, Biochemistry, Atomic and Molecular Physics, and Optics, Instrumentation, Electrical and Electronic Engineering
Sustainable Development Goals: SDG 7 - Affordable and Clean Energy
Electronic version(s): https://doi.org/10.3390/s20072099 (Access: Open)

BibTeX

@article{48c37135953a4c65824f53e9b0170902,
title = "Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions",
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.",
keywords = "Business model, Condition monitoring, Energy revolution, Infrared sensor, Machine learning, Predictive maintenance, Switchgear, Thermal monitoring",
author = "Hoffmann, {Martin W} and Stephan Wildermuth and Ralf Gitzel and Aydin Boyaci and Joerg Gebhardt and Holger Kaul and Ido Amihai and Bodo Forg and Michael Suriyah and Thomas Leibfried and Volker Stich and Jan Hicking and Martin Bremer and Lars Kaminski and Daniel Beverungen and Heiden, {Philipp zur} and Tanja Tornede",
note = "Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2020",
month = apr,
day = "8",
doi = "10.3390/s20072099",
language = "English",
volume = "20",
journal = "Sensors",
issn = "1424-3210",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "7",
}

Publication Details

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

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