Research on parameter identification of load model based on GWO algorithm

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    In order to improve the accuracy of load modeling to meet the requirements of power system simulation calculation accuracy, this paper proposes a load model parameter identification strategy based on the grey wolf optimization (GWO) algorithm from the perspective of overall measurement and identification method. The load model parameter identification strategy takes the substation bus voltage and voltage phase angle as the input when the power grid is disturbed, selects the classic load model of the induction motor parallel ZIP load. The strategy realizes the iterative optimization of the objective function through the gray wolf algorithm to obtain a set of optimal load model parameters, so that the model response can better fit the sample power curve. GWO algorithm has strong fast convergence ability and global search ability. Its application in load modeling parameter identification practice can effectively improve the identification accuracy. By establishing a power system simulation model in PSD-BPA software, two examples are simulated with the disturbance data at the substation bus as the input data of the load modeling. Simulation results show that GWO has obvious advantages in calculation accuracy and convergence speed compared with the commonly used particle swarm optimization algorithm.

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  • Online: May 26,2022
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