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• W4285217218 endingPage "2132" @default.
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• W4285217218 abstract "The increasing penetration of renewable resources via power-electronic converters is turning the modern power grid into a multi-converter system (MCS). In an MCS, most renewable resources currently use grid-following converters (GFLCs) for grid synchronization. The increasing integration of renewable resources via GFLCs can cause small-signal stability problems, especially under weak grid conditions. One way to prevent the stability issue is limiting the capacity of GFLCs in an MCS. Thus, it is important to assess the maximal capacity of GFLCs in the MCS while considering small signal stability constraints (SSSCs). This assessment is challenging due to 1) the complexity of assessing the small signal stability resulting from the complicated interaction between the power network and a large number of GFLCs, especially for a heterogeneous GFLCs with unknown inner parameters; 2) the difficulty of finding the optimal solution to the relevant nonlinear optimization problem for maximal capacity assessment. To address these challenges, this paper proposes a semi-definite programming (SDP)-based method to assess the maximal capacity of GFLCs with SSSCs. In the proposed method, we first formulate the SSSCs based on the generalized short-circuit ratio (gSCR), which is a grid strength metric that can significantly reduce the complexity of quantifying small signal stability in an MCS. Then, we convert the formulated gSCR-based nonlinear optimization problem into an SDP that can conveniently find the optimal solution to the maximal capacity of GFLCs and their optimal allocations in the MCS. The efficacy of the proposed method is demonstrated on a 39-bus test system and a practical wind power system." @default.
• W4285217218 created "2022-07-14" @default.
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• W4285217218 date "2022-10-01" @default.
• W4285217218 modified "2023-09-26" @default.
• W4285217218 title "Assessing Maximal Capacity of Grid-Following Converters With Grid Strength Constraints" @default.
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• W4285217218 doi "https://doi.org/10.1109/tste.2022.3183009" @default.
• W4285217218 hasPublicationYear "2022" @default.
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