Details
| Original language | English |
|---|---|
| Article number | 2099 |
| Journal | Sensors |
| Volume | 20 |
| Issue number | 7 |
| Publication status | Published - 8 Apr 2020 |
| Externally published | Yes |
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
ASJC Scopus subject areas
- Chemistry(all)
- Analytical Chemistry
- Computer Science(all)
- Information Systems
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Sensors, Vol. 20, No. 7, 2099, 08.04.2020.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions
AU - Hoffmann, Martin W
AU - Wildermuth, Stephan
AU - Gitzel, Ralf
AU - Boyaci, Aydin
AU - Gebhardt, Joerg
AU - Kaul, Holger
AU - Amihai, Ido
AU - Forg, Bodo
AU - Suriyah, Michael
AU - Leibfried, Thomas
AU - Stich, Volker
AU - Hicking, Jan
AU - Bremer, Martin
AU - Kaminski, Lars
AU - Beverungen, Daniel
AU - Heiden, Philipp zur
AU - Tornede, Tanja
N1 - Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/4/8
Y1 - 2020/4/8
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.
AB - 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 - Condition monitoring
KW - Energy revolution
KW - Infrared sensor
KW - Machine learning
KW - Predictive maintenance
KW - Switchgear
KW - Thermal monitoring
UR - http://www.scopus.com/inward/record.url?scp=85083277059&partnerID=8YFLogxK
U2 - 10.3390/s20072099
DO - 10.3390/s20072099
M3 - Article
VL - 20
JO - Sensors
JF - Sensors
SN - 1424-3210
IS - 7
M1 - 2099
ER -