The traditional implementation of power scheduling in wind farms mainly adopts yaw control methods. However, whether the yaw process of wind turbine generators can be actually carried out depends on the assessment effect of the safety status of the units (nacelle vibration measurement), which leads to inaccurate reliable power scheduling in actual wind farms. For this purpose, this paper proposes a reliable power scheduling method for wind farms based on the prediction of the vibration acceleration of the nacelle of wind turbine generators. Firstly, aiming at the difficulty in modeling the nacelle vibration mechanism during the yaw process of the unit, a deep neural network is utilized to construct the nacelle vibration acceleration model. Then, under the predictive control framework, combined with the prediction of nacelle vibration acceleration, a reliable power scheduling method for wind farms is proposed to improve the accuracy of power safety scheduling in wind farms by 3%. Finally, a wind farm with 12 wind turbine generators was constructed using Fast.Farm to verify the effectiveness of the method proposed in this paper.