#7 Smart Grid facility management for power quality monitoring

This testbed focuses on smart grid KPI computation performed close to the data sources, with input of
higher-levels info that cannot be accessed locally.
In fact, in smart grids, different information is available at different voltage levels – some is needed locally,
some globally, while other in sub-systems.
Having both real-time requirements, high data volumes and distribution over a large geographical area,
data need to be (pre-)processed locally in context of the overall system state, e.g., SICAM Q200 devices do
record characteristic attributes of the electricity grid with a sampling rate of up to 1MHz.
In industrial systems, integrating power quality monitoring enable the possibility to identify short current
peaks that, otherwise, may lead to system failure.
For example, in a case study, a factory laser robots failure resulted in several production stops; only after
integrating power quality monitoring into the process, it was possible to identify that short current peaks
were the root cause. Establishing prescriptive analytics will help to setup a system that can automatically
react to issues in the power grid. Collected data should be analyzed locally, but integrating all KPIs for
multiple devices into a digital twin is a promising way to significantly optimize results.
This testbed is already active in the framework of the “Aspern Vienna’s Urban Lakeside”, i.e., a smart city
environment of private apartments, a student home, and a school, as well as a supply area with about
6,500 inhabitants and small business, which the IoTwins consortium will be able to exploit for data
collection and configuration tuning.
The testbed in Aspern is a state-of-the-art living lab project that started in 2015. The installed edge devices
are based on the CP-8050 energy automation device from Siemens, that allow runtime installation of the
applications.
The further developments envisioned during IoTwins are:
1. to extend the framework of the CP-8050 from an IoT-cloud interaction towards a three-tier
approach with a more powerful edge platform in-between, which can process power quality
measurements (including the necessary management functionalities);
2. to integrate several monitoring sources towards a cloud-based digital twin providing system-wide
power quality assessments;
3. to integrate off-line generated parameters into distributed twins running at configurable edges,
4. to enable the field devices to annotate their data with an event generator,
5. and to enable edge devices to proactively adapt to predicted outages, e.g., by uploading a full set of
logs for post-mortem root cause analysis.