Load modeling plays a vital role in the operation and control of power system. It is of major practical significance to find out an effective and reliable load modeling parameter identification optimization method. Based on the measurement-based load modeling method

a kind of load model parameter identification strategy based on the improved crown porcupine optimization algorithm (ICPO)

which is based on the good point set

is proposed. The load model parameter identification strategy takes the actually measured voltage data of PMU as input

selects the comprehensive load model

and improves it through the initialization process of good point set optimization to avoid falling into the local optimal value. The objective function is iteratively optimized by the crown porcupine optimization algorithm (CPO) to obtain the optimal comprehensive load model parameters

so that the model can better fit the power curve. It is shown through the analysis of the PMU measured modeling parameter identification examples that the ICPO algorithm proposed in this paper has better performance than the improved differential evolution algorithm (IDE)

particle swarm optimization algorithm (PSO)

gray wolf algorithm (GWO) and CPO algorithm.