Abstract: During low voltage ride through (LVRT), power grid requires wind farm (WF) to provide reactive power to the system. After satisfying the required reactive power output, the remaining capacity of the WF can be used to provide active power to ensure system stability. This paper proposes a combined active and reactive power control method for WF under low voltage ride through based on wind turbines (WTs) aggregation and improves deep deterministic policy gradient (DDPG). First, the active and reactive power control processes of WTs are divided into three stages, based on which the model of the WT control system is established. Then, the WTs are divided into multiple groups according to their attributes. In the process of power distribution, the same control commands are distributed to the WTs belonging to the same group, and this greatly reduces the number of optimization variables and the difficulty of solving optimization problems. Next, a critic-network free based parallel DDPG (CFP-DDPG) deep reinforcement learning method is designed. On this basis, this paper constructs a CFP-DDPG based power distribution framework through defining state, action, evaluation function, model training process and decision-making method. Finally, the effectiveness of the method is verified by using the real WF data in China. The results demonstrate that the WTs aggregation step could help acquire the power distribution quickly and avoid the algorithm falling into the local optimum. And CFP-DDPG could successfully enhance the exploratory ability of the agent through introducing a parallel structure and improving the evaluation method.