## 1 Introduction
Reference [14] addressed the issue of voltage tracking control for inverters under load disturbances and input-output constraints. Based on the time-domain mathematical model of voltage and current in the synchronous rotating coordinate system, it proposed a constraint adaptive PI control strategy based on neural networks. In terms of artificial intelligence involvement in control strategies, ref. [15] constructed a deep learning model for AGC strategy using long short-term memory (LSTM) recurrent neural networks as neurons, and it proposed a data-driven AGC real-time control strategy based on LSTM recurrent neural networks. Additionally, ref. [16] proposed a frequency control strategy for islanded microgrids based on DQN to address the issue of frequency control when microgrids are subjected to strong random disturbances and changes in network topology parameters. Deep reinforcement learning (DRL) integrates deep learning with reinforcement learning, enabling the autonomous learning of optimal strategies through environmental interaction in unknown settings, thus eliminating the need for extensive manual feature engineering or rule design. Currently, several notable applications of DRL have been developed. For instance, ref. [17] employs DRL to propose an automatic power flow adjustment application that considers static stability constraints. This method effectively measures the sensitivity of each device's adjustment to changes in system power flow, thereby accelerating the convergence speed of power flow. Additionally, ref. [18] introduces an optimal configuration strategy for system measurement devices based on observability strength and proposes a GCN-DDPG algorithm to ensure system observability across multiple scenarios. Moreover, some researchers have applied DRL to achieve automatic generation control (AGC). Considering that DC modulation has been widely used to improve the frequency stability of the system, while meeting the basic requirements for large-scale conventional energy and wind power transmission, making full use of the fast controllability of DC transmission power in order to coordinate the active power control can improve the stability of the wind power delivery, as well as greatly improving the utilization rate of the DC transmission system and the capacity of wind power transmission [19; 20; 21]. To enhance the frequency response capability of high-proportion wind power electrical systems and maintain system frequency stability, research on how wind power participates in system frequency regulation mainly focuses on using additional control of wind turbine generators to respond to changes in system frequency [22]. In [23], an enhanced strategy for HVDC transmission to engage in auxiliary frequency control was introduced and experimentally validated, and the results indicated that employing HVDC instead of relying on wind power frequency regulation reserves can significantly improve system frequency stability. Reference [24] focused on improving the poor system stability when constant voltage and frequency (V/F) control was used for a direct-drive wind farm side converter station via a flexible DC transmission system. Considering the LCC-VSC hybrid DC transmission system, this paper proposes a frequency coordination control strategy applicable to wind power transmission systems based on hybrid DC transmission technology. Finally, simulations are conducted using PSCAD/EMTDC, and the feasibility and effectiveness of the control strategy are validated by comparing with the system response without AFC. The specific innovative contributions are as follows:  
For the sending end, an additional frequency controller is designed based on the VFF-RLS algorithm and root locus method. It is proposed to use AFC in coordination with AGC using conventional PI control to suppress frequency fluctuations caused by changes in wind power output.