The optimal adjustment of reactive power compensation device such as capacitors can not only reduce the network losses， but also present influences on harmonic power flow and harmonic power losses. Wind generators can produce harmonic pollution and present impacts on harmonic power flow and harmonic power losses. However， the effects of the harmonic characteristic and output uncertainty of wind power on harmonic power flow and harmonic power losses are not considered simultaneously in the traditional reactive power optimization methods， which may result in the violation of harmonic standard and is adverse to network losses reduction. In this regard， this paper proposes the reactive power stochastic optimization model for power systems considering the impact of the wind power harmonics. In this model， the uncertainty of wind power is modeled by the scenario method， and the base-frequency network losses and the harmonic losses are considered in the objective function. Also， the constraints such as base-frequency power flow equations， the harmonic power flow equations and the total harmonic voltage distortion constraint are incorporated into the proposed model. After that， focusing on the proposed reactive power stochastic optimization model， the highly efficient method combining the adjustable driving force-based particle swarm optimization and a fully-connected deep neural network is proposed in this paper. Finally， the effectiveness of the proposed model and method is validated by three modified IEEE test systems.