An Improved Particle Swarm Optimization Algorithm for Parameters Identification of Power Load Model Based on Simulated Annealing

摘要

To improve the parameters identification precision of power system load model, an improved Particle Swarm Optimization algorithm based on simulated annealing (SA-WPSO) is proposed. Firstly, the algorithm adaptively adjusts the inertia weight to improve the search speed of the whole process according to the particles in different search periods. Secondly, we combine the simulated annealing (SA) algorithm and Particle Swarm Optimization (PSO) algorithm to make the algorithm converge to the global optimal value. Finally, the improved algorithm was applied to parameters identification of power load model. Simulation experiments show that the algorithm has effectively improved the parameters identification precision and can converge to the global optimal value rapidly in the parameters identification of static load model.