## 6 Conclusions  
Fault localization and detection are well established topics in the research literature. However, not much attention was paid to the fact that DNs, as opposed to TNs, generally lack the dedicated communication infrastructure and, consequently, how this could adversely affect the corresponding monitoring, control or analysis schemes. In this paper, the authors developed an edge-cloud computing framework specifically aimed at addressing potential issues with the reliability of the measurement data transfer, arising from using LTE communication technology. Since the proposed framework separates time-critical applications, by moving them to the edge, from near real-time applications, the reliability of schemes such as fault localization can also be ensured in DNs. Furthermore, by implementing event detection at the edge, the measurements are sent to the control center in full resolution only in case of events, which significantly reduces the amount of transferred data. With this, the DSO's expenses are lowered, whereas the observability of the grid is not negatively affected since during the steady-state operation of the EPS, the data are generally redundant. Another important quality of the established framework is that it is completely automated; the DSO is informed about the fault and its location in near real-time, without any of the experts having to look at the data. Lastly, the proposed framework provides the capability of easy integration of other time-critical applications at the edge in the future.  
Conceptualization, A.C. and D.S.; methodology, M.M. and D.S.; software, M.M. and A.C.; validation, M.M. and D.S.; formal analysis, D.S.; investigation, A.C. and D.S.; resources, M.S.; data curation, M.M and D.S.; writing--original draft preparation, D.S.; writing--review and editing, A.C., U.R. and D.S.; visualization, M.M. and D.S.; supervision, U.R. and A.C.; project administration, M.S. and D.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Component** & **Minimum Value [ms]** & **Maximum Value [ms]** & **Average Value [ms]** \\ \hline \(t_{1}\) & 1.09 & 3.46 & 1.42 \\ \hline \(t_{2}\) & 54.92 & 191.27 & 71.13 \\ \hline \(t_{3}\) & 1384.22 & 4096.42 & 1867.86 \\ \hline \(t_{4}\) & 57.04 & 421.74 & 101.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum, maximum and average time delay values for each component of the FLA.  
Funding: This research was partly funded by the Slovenian Research Agency, grant number J2-9232, and the European Commission through the H2020 projects RESOLVD (Grant no. 773715) and PLATOON (Grant no. 872592). The authors would like to thank them for the financial support. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors would like to thank [PERSON] from EyPESA for his support and providing the access to the pilot in order to produce this work. Conflicts of Interest: The authors declare no conflict of interest. Nomenclature PMU Phasor Measurement Unit PDC Phasor Data Concentrator TN Transmission network DN Distribution network LTE Long-Term Evolution EPS Electric power system RES Renewable energy source DSO Distribution System Operator WAMS Wide-Area Monitoring System WAMPAC Wide-Area Monitoring, Protection and Control QoS Quality of Service TCP Transmission Control Protocol UDP User Datagram Protocol PQM Power Quality Meter SM Smart Meter