At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.

The grid integration of large-scale wind power will alter the dynamic characteristics of the original system and the power distribution among synchronous machines. Meanwhile, the interaction between wind turbines and synchronous machines will affect the damping oscillation characteristics of the system. The additional damping control of traditional synchronous generators provides an important means for wind turbines to enhance the damping characteristics of the system. To improve the low frequency oscillation characteristics of wind power grid-connected power systems, this paper adds a parallel virtual impedance link to the traditional damping controller and designs a DFIG-PSS-VI controller. In the designed controller, the turbine active power difference is chosen as the input signal based on residual analysis, and the output signal is fed back to the reactive power control loop to obtain the rotor voltage quadrature component. With DigSILENT/PowerFactory, the influence of the controller parameters is analyzed. In addition, based on different tie-line transmission powers, the impact of the controller on the low-frequency oscillation characteristics of the power system is examined through utilizing the characteristic root and time domain simulation analysis.