Crowbar conduction has an impact on the transient characteristics of a doubly fed induction generator (DFIG) in the short-circuit fault condition. But crowbar protection is seldom considered in the aggregation method for equivalent modeling of DFIG-based wind power plants (WPPs). In this paper, the relationship between the growth of postfault rotor current and the amplitude of the terminal voltage dip is studied by analyzing the rotor current characteristics of a DFIG during the fault process. Then, a terminal voltage dip criterion which can identify crowbar conduction is proposed. Considering the different grid connection structures for single DFIG and WPP, the criterion is revised and the crowbar conduction is judged depending on the revised criterion. Furthermore, an aggregation model of the WPP is established based on the division principle of crowbar conduction. Finally, the proposed equivalent WPP is simulated on a DIgSILENT PowerFactory platform and the results are compared with those of the traditional equivalent WPPs and the detailed WPP. The simulation results show the effectiveness of the method for equivalent modeling of DFIG-based WPP when crowbar protection is also taken into account.
With the continuous increment of photovoltaic (PV) energy connection into a power grid, the accuracy of control parameters of PV power generation systems becomes the key to the stable operation of the power grid. At present, parameter identification based on an intelligent algorithm is a common means to obtain control parameters. However, most of the data used for identification are simulation data and the identified parameters are difficult to use in practical engineering. Therefore, aiming at the acquisition of low voltage ride through (LVRT) control parameters of PV unit, a method of identification of LVRT parameters of the PV unit is proposed, which combines sensitivity analysis with field measurement. In this paper, the test scheme of the required data is put forward through sensitivity analysis of the identified parameters, and the intelligent algorithm is used to identify the low voltage traverse parameters of the data. Finally, the optimal value is extracted from the identification results and substituted into the model. The accuracy of the parameter identification results is verified by calculating the error between the output of the model and the real operation data. The method considers the errors caused by different power levels of the inverters with highly accurate and consistent identification results, which is applicable to practical engineering calculation.
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