Refined identification of the key parameters of power system synthesis load model based on the improved butterfly algorithm - Wang, Zongyao; Yan, Gaoyang; Rong, Yi; Wang, Han (Frontiers in Energy Research, 2024)
With the improvement of power grid simulation accuracy requirements, the existing typical load model parameters can no longer meet the accuracy requirements and become the short board that restricts the stable operation of power system. This paper mainly proposes an improved butterfly optimization algorithm based on population optimization and dynamic strategy (PODSBOA) for commonly used synthesis load model (SLM) parameters to realize the refined and personalized identification of SLM key parameters:The results show that, in the 2s load data experiment, the identification error is 0.02, the identification accuracy is 4.09, and the convergence time of PODSBOA is 12.048s. In the 5s load data experiment, the identification error is 0.013, the identification accuracy is 6.65, and the convergence time of PODSBOA is 23.405s, the identification errors in the two sets of experiments are reduced by 0.02023-0.06443 compared with other algorithms. The comparison results of different load model parameter identification algorithms show that the improved PODSBOA proposed in this paper has high recognition accuracy and fast convergence speed, and solves the problem of low accuracy and instability of the identification results of the existing identification schemes.