Martin W. Hoffmann, Stephan Wildermuth, Ralf Gitzel, Aydin Boyaci, Jörg Gebhardt, Holger Kaul, Ido Amihai, Bodo Forg, Michael Suriyah, Thomas Liebfried, Volker Stich, Jan Hicking, Martin Bremer, Lars Kaminski, Daniel Beverungen, Philipp zur Heiden, Tanja Tornede
- 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.
