As for the slow convergence speed after the introduction of cross over and mutation operators in the reactive power optimization of adaptive particle swarm

the logistic chaotic mapping is introduced into the adaptive particle swarm optimization algorithm

and the logistic chaotic optimization of the particle position of the adaption value

the most optimization position is performed through the calculation of particle adaption value. The simulation calculation is made through IEEE 14 and IEEE 57 node system. It is verified with minimum optimization network loss as optimization objective that the algorithm can improve the efficiency and diversity of the particle value

can keep the control variable of the reactive power optimization jump quickly out of the local optimum and speed up convergence speed of the algorithm and have a definite reference significance for the solving the reactive power optimization of the power system.