NIU Xuefei, MA Yingxin, FAN Dengbo, et al. A Fault Transient Control Parameter Identification Method for New Energy Grid-connected Inverter Based on Phase-locked Loop Dynamic Model[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2024.0318
Citation: NIU Xuefei, MA Yingxin, FAN Dengbo, et al. A Fault Transient Control Parameter Identification Method for New Energy Grid-connected Inverter Based on Phase-locked Loop Dynamic Model[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2024.0318

A Fault Transient Control Parameter Identification Method for New Energy Grid-connected Inverter Based on Phase-locked Loop Dynamic Model

  • The analysis and calculation of fault characteristics of new energy power supply systems depend on the accurate transient model and control parameters of their grid-connected inverters. However, the existing fault transient control parameter identification methods for grid-connected inverters of new energy sources fail to adequately consider the dynamic response of the nonlinear link of the control system during the transient period, resulting in a considerable error in the parameter identification results. The established fault transient model struggles to simulate the fault characteristics of the real site, and it is difficult to support the analysis and research of the fault characteristics of new energy sources. Aiming to address this issue, in this paper we propose a parameter identification method for new energy grid-connected inverters considering the dynamic model of phase-locked loop. The influence of the dynamic process of the phase-locked loop on the identification model during the fault transient period is thoroughly analyzed. In addition, a fault transient current identification model is developed with the dynamic response of the nonlinear link during the fault period taken into account. The accurate identification of the control parameters is realized by fitting the output waveform based on the adaptive particle swarm optimization (PSO) algorithm. Simulation results confirm that the identification error of the proposed parameter identification method is less than 5%.
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