## 6 Discussion and Prospects  
To comply with the new primary frequency security standards for the power system, this paper proposes a combined inertial intelligent control strategy of wind turbines based on CAE and DNN. This method can effectively extract the key features of the optimal frequency regulation parameters for online fast frequency regulation parameter decision-making. It has a faster computational speed, good prediction accuracy, and generalization capability. This method can provide theoretical reference for new energy to participate in grid frequency control. The effect of combined inertial control mainly depends on the control parameters, including \(K_{\text{pf}}\) and \(K_{\text{df}}\). The above case study shows that different values of the control parameters can lead to different effects of the control of frequency. The optimal parameters vary with different factors and scenes, which need a great amount of time and calculation to be obtained. The method presented in this paper offers significant advantages over traditional time-domain simulations in terms of both online decision-making time for frequency control and accuracy in achieving optimal combined inertial control for frequency regulation. In practical production, different application scenarios need to be faced, and these scenarios can be roughly represented by wind speed, wind power proportion, and the load disturbance level, which the wind turbine needs to cope with in case of emergencies. The method proposed in this paper can effectively take into account the impact of varying wind speed, wind power proportion, and load disturbance on the frequency response characteristics of the power system, thereby meeting the requirements of different scenarios and demonstrating its high versatility. The simulation results validate the efficacy of the proposed method across a wide range of scenarios. Shallow BP neural networks primarily rely on manual feature selection and design to accomplish feature learning. For simple classification tasks, shallow BP neural networks can obtain effective features through preprocessing and feature selection of data. However, for more complex tasks, manual feature design becomes very difficult, leading to the traditional shallow BP neural networks being unable to fully utilize their maximum feature learning capabilities. The presented method adds CAE-based feature learning before network training, which can learn effective data features, explore the implicit information of data, facilitate better fitting of DNN, and improve the speed of obtaining network parameters. According to the comparison frequency curves of different parameters, the  
Figure 10: Frequency response curves (**a**) with different parameters (**b**) with different load disturbances. combined inertial control by the presented method in this paper achieves the best frequency regulation effect. Traditional time-domain simulation is a time-based simulation method, which has the advantages of high accuracy, high reliability, and high realism. However, in situations where real-time response is required, such as in the field of power systems discussed in this article, its real-time performance is poor and cannot meet the requirements of real-time simulation. This paper provides a new fast technical method for the online frequency control of wind power after load disturbance events. Compared with other combined inertial control methods, this method is more time-saving and generalized for different and complex operation conditions of new power systems. This paper proposes an improved combined inertial intelligent control of wind turbines for temporary frequency support based on CAE and DNN. In fact, there are many other methods of fan rotor kinetic energy control, such as stepwise inertial control (SIC) and so on [23; 24; 25]. In further research, other frequency control methods will be studied to make frequency control more efficient.