Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (π model) has high accuracy, its parameters are too many. In order to improve the identification efficiency and reduce the difficulty of identification, a simplified model identification strategy based on parameter sensitivity analysis is proposed. Firstly, based on the global sensitivity analysis, the sensitivity analysis of the model parameters is carried out to obtain the First Order Sensitivity Indices (FSI)and Total Sensitivity Indices (TSI). Secondly, the FSI and TSI of each parameter are analyzed, and the effect on the output of model of each parameter is determined by FSI. For less influential parameters, whether the parameter should be fixed as constant is determined by the value of TSI. The parameter whose TSI equal or approximately equal to zero should be fixed as a constant. Finally, the improved genetic algorithm is used to identify the parameter-simplified model, and the effectiveness of the simplified identification strategy is verified by comparing the fitting effects with the measured curve and the residual and the integrated parameter models.

No abstract

For the safe and fast recovery of line commutated converter based high-voltage direct current (LCC-HVDC) transmission systems after faults, a DC current order optimization based strategy is proposed. Considering the constraint of electric and control quantities, the DC current order with the maximum active power transfer is calculated by Thevenin equivalent parameters (TEPs) and quasi-state equations of LCC-HVDC transmission systems. Meanwhile, to mitigate the subsequent commutation failures (SCFs) that may come with the fault recovery process, the maximum DC current order that avoids SCFs is calculated through imaginary commutation process. Finally, the minimum value of the two DC current orders is sent to the control system. Simulation results based on PSCAD/EMT-DC show that the proposed strategy mitigates SCFs effectively and exhibits good performance in recovery.

No abstract

No abstract