Dynamic Load Model Parameter Identification Based on Genetic Simulated Annealing Hybrid Algorithm

Optimal algorithm is an important factor for identifying the dynamic load model parameters accurately which with the nonlinear characteristic. The paper adopts the improved genetic algorithm with optimal reservation strategy (OGA) at the beginning and finds that the problem of convergent to local optimal solution is still exist. Considering the Simulated Annealing algorithm (SA) has good performance in respect of local searching characteristic which can prevent convergence from local optimal, so the hybrid algorithm combined with SA algorithm and GA algorithm (GASA) is proposed in this paper. Case studies showed it can improve the accuracy of parameter identification and reduce the P Q fitting errors .The efficiencies of identifying substation load model based on the PMU measure data is proved as well.