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• W3203162421 startingPage "108085" @default.
• W3203162421 abstract "Power systems as critical infrastructure are an integral part of human society and are therefore of paramount importance to modern life. Vulnerabilities in the system, that are revealed either by accidental or deliberate events, can cause large losses of power supply with sever social and economic consequences. A tool that identifies the vulnerabilities in a power system can provide the operators the means to support reliable power system operations. This paper presents a methodology for power system vulnerability assessment that couples an AC based cascading failure simulation model and a meta-heuristic optimization procedure. The objectives of the assessment are to (1) rank the most important branches in the transmission grid, and (2) identify sets of branches if simultaneously tripped will cause the cascade with highest intensity. The first objective is achieved by ranking the criticality of the branches using two criteria (i) the impact that each branch failure has on the DNS and (ii) the frequency of line overload. The second objective is achieved by hard linking an AC based cascading failure simulation model and a meta-heuristic based optimization procedure. The methodology allows the generation and the identification of vulnerability scenarios, and therefore, provides insights that can be used by operators in developing strategies to minimize the effects of accidental and deliberate events. The algorithm developed for the purpose of this study is applied to the IEEE 118-bus test system and the Swiss power grid. The results demonstrate the capability of the proposed methodology for assessing power system vulnerability." @default.
• W3203162421 created "2021-10-11" @default.
• W3203162421 creator A5019949573 @default.
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• W3203162421 date "2022-01-01" @default.
• W3203162421 modified "2023-10-05" @default.
• W3203162421 title "Identifying and assessing power system vulnerabilities to transmission asset outages via cascading failure analysis" @default.
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• W3203162421 doi "https://doi.org/10.1016/j.ress.2021.108085" @default.
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