The focus is initially based on a line of research into smart grid communication and powerline-based network analysis. Projects such as OPERA-1 and OPERA-2, as well as E-DeMa, moma and ELABOX, investigated the use and further development of PLC and smart metering in the energy sector. ENERGIE followed with applications at the low-voltage level with the aim of recording network status variables via G3-PLC. Building on this, model-based planning methods and measurement approaches were developed in STeP. NExt STeP continues this line of research and systematically anchors data science, statistics, machine learning and AI in data-driven condition assessment, particularly for utilisation and ageing diagnostics, as well as in anomaly detection.

The second line of development has its roots in the former Power Electronics Department, which was established as part of the interdisciplinary RHeNoHaft project. The focus was on the design and evaluation of power electronic systems and the physical characterisation of materials and components. Electrical, dielectric and thermal properties were investigated, supplemented by dielectric strength and performance diagnostics of in-house developed assemblies. The development of standard-compliant test benches and integrated measurement chains gave rise to a working method in which measurement data is interpreted in terms of causes and models are experimentally validated. This metrological and physical approach is directly transferred to SMS-Energie and today forms a key basis for condition assessment, in particular for statements on the utilisation and ageing of networks, components and materials.

SMS-Energie combines physics, electronics, communication technology and data science into an integrated research approach. Measurement systems and evaluation methods for condition assessment, anomaly detection and predictive maintenance of electrical distribution networks are developed and validated. The focus is on the further development and expansion of established technologies into practical measurement systems, from design, hardware, firmware and software to scalable data acquisition in the laboratory and in the field. One focus is the expanded use of powerline technology. In addition to its communication function, it can provide diagnostic information about the network status via changes in the transmission channel, enabling conclusions to be drawn about the thermal load, ageing and anomalies of components. Data is evaluated using descriptive statistics, machine learning and artificial intelligence methods. The aim is to provide a reliable basis for decision-making for safe, efficient and sustainable grid operation. To this end, we are researching methods, measurement and diagnostic technology and their applications for use in energy grids.

The department works closely with partners from science and industry. The research area Smart Metering Systems for Energy Networks is headed and represented by Thorsten Klauke-Queder. The team includes Nora Nieß, Chris Fonteyn and Sven Spieß. The team is also supported by student assistants Duygu Bilir and Andrew Nugroho.