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.
Highlights
Germany’s energy policy requires the electricity system to be more efficient, environmentally friendly, and a source of affordable energy for everyone [1,2]
We describe our approach to predictive maintenance of medium voltage switchgear systems
To develop a business model suitable for a computerized maintenance management system (CMMS) based on predictive maintenance using artificial intelligence, we plan to apply a standard for service system engineering [109]
Summary
Germany’s energy policy requires the electricity system to be more efficient, environmentally friendly, and a source of affordable energy for everyone [1,2]. In the present use-case, the medium-voltage switchgear is the physical asset, the condition data can be considered its digital twin in the virtual world, and the predictive maintenance applications are the smart service. The use-case is currently limited to the operations and maintenance life-cycle phase but may utilize data from other phases in the development of the smart service, i.e., simulation know-how of the switchgear for the development of machine learning applications. This is mostly due to the brownfield market, and retrofit solutions are to be favored over novel systems addressing greenfield installations.
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