Dependability Modeling and Analysis of 5G Based Monitoring System in Distribution Grids

Amare, Tesfaye; Garau, Michele; Helvik, Bjarne E.

doi:10.1145/3306309.3306334

Cited by 1 publication

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“…Below, some important atomic model types for the 5G architecture are presented. For a more detailed description of the atomic models, refer to [42].…”

Section: Model Descriptionmentioning

confidence: 99%

Effect of Communication Failures on State Estimation of 5G-Enabled Smart Grid

2020

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Information and Communication Technologies (ICT), Wide Area Measurement Systems (WAMS) and state estimation represent the key-tools for achieving a reliable and accurate knowledge of the power grid, and represent the foundation of an information-based operation of Smart Grids. Nevertheless, ICT brings new potential vulnerabilities within the power grid operation, that need to be evaluated. The strong interdependence between power system and ICT systems requires new methodologies for modeling the smart grid as a Cyber Physical System (CPS), and finally analyzing the impact of ICT failures on the power grid operation. This paper proposes a novel methodological approach that combines Stochastic Activity Networks (SAN) modeling and numerical computation for dependability analysis of a 5G-based WAMS. Internal influences such as component failures and external influences such as rain effect are considered, and the impact of these failures are assessed over the WAMS capability to provide reliable data for performing an accurate power network state estimation. Different state estimation approaches (traditional SCADA and PMUbased algorithms) and weather conditions are compared in terms of mean states estimation error and safety. The results highlight that 5G based WAMS result in a close-to-ideal behavior which enforces the prospect of a future adoption for smart grid monitoring applications.

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“…Below, some important atomic model types for the 5G architecture are presented. For a more detailed description of the atomic models, refer to [42].…”

Section: Model Descriptionmentioning

confidence: 99%