## 5 Proposed Edge-Cloud Framework for Automated Fault Detection and Localization  
### Overview and Functionalities  
The main driving force behind the development of the proposed edge-cloud framework for the FLA was the concern about the reliability of the PMU data transmission over LTE (as explained in Section 2). Since missing data could adversely influence the performance of the FLA, we decided to store the full granularity (200 Hz) of the PMU measurements locally at the edge and simultaneously process them for fault detection purposes. For the observability of the grid, on the other hand, we decided to lower the reporting rate of all measuring devices down to 1 Hz in order to reduce the amount of data transfer from the edge nodes to the control center and, consequently, reduce the DSO's bills for data transmission over LTE. The premise for doing so is that the grid is in a steady-state condition most of the time, during which it is acceptable to sacrifice some of the DSO's resolution of the grid's observability (WAMS for instance) on account of the reduced burden imposed on the communication infrastructure. In any case, the full resolution of data during steady state is more or less redundant and does not provide any additional information. However, this changes during the event when the full granularity of the PMU measurements is necessary in order to capture the transient nature of the event. Therefore, the full granularity of measurements (without any missing data) must still be accessible to the control center since those can help the DSO to better understand the event's circumstances and are also crucial for the reliable operation of the FLA.  
To achieve computing capability at the edge, we complemented each of the PMUs with a co-located embedded personal computer (ePC), whose importance is twofold. Firstly, the ePC serves as a temporal data storage unit for the corresponding PMU and, secondly, the ePC provides the capability for real-time analysis of the data. The latter functionality enables the detection of events on the entirety of the PMU data by comparing the obtained measurements with the pre-defined thresholds of events. Whenever an event is detected, a precise timestamp of the event's inception moment is automatically reported and stored  
Figure 4: Single-line diagram of a 16-bus distribution feeder located in Spain with the locations of measuring devices. in the cloud's side of the proposed framework. A query for data (the full resolution of data for a short time window around the timestamp) is then issued to the PMU and PQM databases. Once all the required data from the edge is reported back to the cloud, the FLA takes place. The algorithm's results for the corresponding event are automatically sent to the DSO's control center where they are visualized and stored in the events log. The advantage of the approach described above is not only in preserving the granularity of the measurements in the case of events, but also that the detection and reporting of events to the control center is run in real-time, whereas the comprehensive analytics of events can be scheduled for later. This is not only extremely useful in the case where the second event would occur before the analysis of the first is finished, but inherently also allows the DSO to perform some other form of analysis on event measurements that is computationally more demanding than the described FLA and, therefore, cannot be carried out in real-time (e.g., machine-learning methods). The only limitation is that the analysis of the event has to be completed within the retention policy of the time-series database on ePCs (in our case set to 7 days). Another important aspect worth mentioning is that while the chance of an event in a power system coinciding with the unavailability of a communication (LTE) network is tiny, it is still not negligible. Normally in such a case, all the measurements would be lost and the DSO would have no awareness of the event.