With the increasing complexity and demands of modern power grids, reliable and flexible communication within the energy infrastructure is becoming crucial. The growing number of smart meters and the increased amount of data create additional challenges in ensuring stable connectivity and accurate information transfer. Therefore, effective monitoring and diagnostics of network communication are crucial for the smooth operation of the metering infrastructure, as only timely detection of errors and optimization of connections enable reliable data collection and energy management. Specialized devices for monitoring network traffic, such as G3-PLC analyzers, are often expensive and require additional infrastructure adjustments. The master's thesis focuses on the analysis and diagnostics of communication in G3-PLC networks, especially within the framework of advanced metering infrastructure. The work examines the role of data concentrators, such as the AC750, in ensuring appropriate and efficient diagnostics of the G3-PLC network. The disadvantage of diagnostics of existing methods using the AC750 device is that they do not have more thorough customized statistics and additional methods for traffic analysis.
The central part of the master's thesis is the development of an enhanced diagnostic method for network traffic analysis using the Wireshark tool on an existing data concentrator infrastructure. The concentrator, acting as a sniffer, forwards network traffic to the Wireshark, in which we have implemented customized dissectors. In this way, the proposed method enables statistical analysis of network traffic, necessary for optimizing network performance. The obtained results are then clearly displayed in the Wireshark tool. The work also compares existing network analysis tools, such as MAX79356 G3-PLC Sniffer Kit, Neuron nBox-Tool and Trialog G3-PLC Analyzer, with the aim of highlighting the advantages of the proposed solution.
The results of the master's thesis demonstrate that the integration of advanced network diagnostics directly into data concentrators improves the capabilities and efficiency of monitoring network traffic and troubleshooting power grid communication problems. The findings contribute to the broader field of smart grid management by offering an efficient and cost-effective solution for real-time network traffic diagnostics.
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