Optimizing PWM Switching Sequence of Inverters Using an Immune Genetic Algorithm

摘要

Aiming at the disadvantages of genetic algorithms (GAs), such as slow convergence, easy prematuration and so on, in finding optimal PWM switching sequence of inverters. This paper proposes an improved immune genetic algorithms (IGOAs) to optimize the switching sequence of inverters by using the integral of square of the difference between output current and sinusoidal reference current as objective function. IGOAs takes advantage of adaptive mutation probability and T cell-mediated operators to increase the diversity of individuals and improve convergence during evolution process. Four random resistances in the load side of inverters are considered in numerical experiments. Simulation results show that IGOAs can track reference current and obtain low total harmonic distortion (THD) when resistance value is exposed to random perturbations.