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• W4323020817 abstract "The increasing integration of Power Electronics (PE)-based renewable energy sources into the electric power system has significantly affected the traditional levels and characteristics of fault currents compared to the ones observed in power systems dominated by synchronous generating units. The secure operation of a renewable rich power system requires the proper estimation of fault currents with wide range of scenarios of the high share of renewables. Although the utilization of detailed and complex time-domain dynamic simulations allows for calculating the fault currents, the resulting modeling complexity and computational burden might not be adequate from the operational perspective. Thus, it is necessary to develop alternative quicker data-driven fault current estimation approaches to support the system operator. For this purpose, this paper utilizes an Artificial Neural Network (ANN)-based tool to estimate the characteristics of short circuit currents in power systems with high penetration of power electronics-based renewables. The short circuits against different penetration of renewables are produced offline using the DIgSILENT PowerFactory considering the control requirements for renewables (e.g., fault ride through requirement). The resulting dataset is utilized to train the ANN to provide the mapping between the penetration level and the characteristics of the short circuit currents. The application of the approach using the modified IEEE 9-bus test system demonstrates its effectiveness to estimate the components of short circuit currents (sub-transient current, transient current, and peak current) with high accuracy based only on the penetration of power electronics-based renewables." @default.
• W4323020817 created "2023-03-04" @default.
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• W4323020817 date "2023-01-01" @default.
• W4323020817 modified "2023-10-01" @default.
• W4323020817 title "Application of Artificial Neural Network-Based Tool for Short Circuit Currents Estimation in Power Systems With High Penetration of Power Electronics-Based Renewables" @default.
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• W4323020817 doi "https://doi.org/10.1109/access.2023.3249296" @default.
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