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

Sensors

Stephan Wildermuth¹  MartinW. Hoffmann¹  Ido Amihai¹  Jörg Gebhardt¹  Aydin Boyaci¹  Holger Kaul¹  Ralf Gitzel¹  Daniel Beverungen²  Philipp zur Heiden²  Jan Hicking³  Martin Bremer³  Lars Kaminski³  Volker Stich³  Bodo Forg⁴  Michael Suriyah⁵  Thomas Leibfried⁵  Tanja Tornede⁶ 

[1] ABB AG, Corporate Research Germany, 68526 Ladenburg, Germany;Chair of Business Information Systems, Paderborn University, 33098 Paderborn, Germany;FIR (Institute for Industrial Management) at the RWTH Aachen University, 52074Aachen, Germany;Heimann Sensor GmbH, 01109 Dresden, Germany;Institute of Electric Energy Systems and High Voltage Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany;Software Innovation Campus Paderborn, Department of Computer Science and Heinz Nixdorf Institute, Paderborn University, 33098 Paderborn, Germany;

关键词: energy revolution;    condition monitoring;    switchgear;    infrared sensor;    predictive maintenance;    machine learning;   

DOI  :  10.3390/s20072099

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown