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• W4377103924 abstract "With the increasing proportion of renewable energy in the new power system, the grid-connected capacity of photovoltaic (PV) units shows an obvious upward trend, but its dynamic behavior under different penetration rates significantly affects the transient stability of the power system, so it is crucial to establish a dynamic model that meets the actual working conditions and select a suitable parameter identification method. Therefore, in this paper, based on the electromechanical transient characteristics of the grid-connected PV power generation system, the corresponding dynamic discrete equivalent model is established, and the simulation platform of the grid-connected PV power generation system is built in MATLAB/Simulink to study the adaptability of the dynamic discrete equivalent model of the grid-connected PV power generation system from the single and multiple scenarios using the ordinary least squares (OLS) and bat algorithm (BA) while comparing the generalization ability of the parameters identified by the two methods to the model. The simulation results show that the generalization ability of the parameters identified by the OLS and BA for the model in the single scenario is better, indicating that the model has good adaptability; the generalization ability of a set of general parameters identified by the BA for the model in the multiple scenarios is better than that of the OLS, indicating that the parameters identified by the BA have better adaptability. In conclusion, the dynamic discrete equivalent model of the grid-connected PV power generation system proposed in this paper can accurately reflect the dynamic characteristics of the grid-connected PV power generation system, and the parameters identified by the BA are more generalized than the OLS." @default.
• W4377103924 created "2023-05-20" @default.
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• W4377103924 date "2023-05-17" @default.
• W4377103924 modified "2023-09-30" @default.
• W4377103924 title "Research on Dynamic Modeling and Parameter Identification of the Grid-Connected PV Power Generation System" @default.
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• W4377103924 doi "https://doi.org/10.3390/en16104152" @default.
• W4377103924 hasPublicationYear "2023" @default.
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