Inductance Parameter Identification of Buck Converter Based on Differential Evolution Algorithm

Abstract:
Differential evolution (DE) is an efficient global optimization algorithm based on group heuristic search. It has the advantages of simple principle, few controlled parameters, easy implementation, fast convergence speed and strong robustness. Aiming at the problem that the change of inductance value affects the control performance of buck converter, an inductance parameter identification based on differential evolution algorithm is proposed. Firstly, an on-line multi-parameter identification system of Buck converter is established, and then the small signal parameter models of inductance current and capacitance voltage are analyzed and derived. Then, the differential evolution algorithm is used to update the inductance parameters of the small signal model with the difference between the actual output value of the inductance current and the observed value as the objective function, so as to obtain the optimal estimation value of the inductance. Finally, differential evolution algorithm and recursive least square method are compared to identify the system parameters. The simulation results show that under transient conditions, the differential evolution algorithm can accurately and quickly identify the inductance value within the allowable error range, and has higher estimation accuracy and faster tracking speed than the recursive least square method.