Abstract

With the increasing penetration of renewable generation in low voltage distribution, the edge of the power grid is slowly replacing classical, large-scale power stations, taking up roles and responsibilities that were unimaginable only a few years ago. However, while feed-in on the 400V level is now commonplace, grid state and power quality monitoring still lag far behind because of obvious cost and complexity reasons. In fact, only very few parts of Germanys approx. 1,1 Mln. long 400V distribution grid are actively monitored today and even basic grid quality parameters such as the voltage level at the end of the line are typically unknown. Driving down the cost per measurement point and the complexity of data analysis is thus of paramount importance for enabling true wide-area monitoring of the LV power grid. The authors explore the feasibility of this goal by examining the performance of a nonconventional measurement system. It consists of a network of Internet-of-Things (IOT)-based power quality sensors, connected to a cloud-based big data analysis platform. Specifically, measurement nodes comprise of voltage sensors attached to Android smartphones and Linux-based WLAN access points. Sensor data is automatically uploaded to the cloud system with the MQTT protocol. First results from a field trial in a rural area in Germany indicate good data quality and show excellent promise for detailed assessments of the edge of the power grid.