To overcome the problem of low accuracy and easily falling into local optimum

the local extreme caused by too much variety and high dimension of control variables

it proposes a new type of particle swarm algorithm

based on virtual extreme for reactive power optimization. The algorithm adopts the Monte Carlo simulation technology to the initial population selection to ensure the diversity of value. The impact factor was added

and the optimization parameters were set according to the control variables. The virtual global extreme was introduced to help particles jump out of the local optimum. At last

the proposed algorithm was applied to the IEEE-14-bus system. The comparative results show that this virtual extreme particle swarm optimization algorithm has better search capability and higher degree of convergence for reactive power optimization.