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.

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Log responses in crystalline rocks are a function of the lithology (rock matrix), macro- and microfractures. Basically, the porespace-geometry dependent petrophysical parameters (porosity, internal surface area, permeability, formation factor) are similarly interrelated as in sediments, but mineralogy and fractures exert greater control than in sediments. A qualitative log interpretation method was developed and tested. Fractured and otherwise deteriorated zones with anomalous properties are identified, using a suitable combination of five standard logs. Before a quantitative well-log analysis of petrophysical parameters is possible, the logs are corrected for lithology effects, using a quick-look lithology identification program. For lithology identification seven logs are considered sufficient. Their mean lithology-dependent response in the German KTB borehole is reported.

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