## 1 Introduction  
Wind power has been widely used because of its advantages such as environmentally friendly, mature technology and low cost [1]. With the development of wind power generation technology, the penetration rate of wind power in various countries has been rising. According to relevant data, by the end of 2020, China's installed wind power capacity accounted for 12.79%, showing a rapid growth rate [2]. The WT is generally connected to the grid through a converter, which leads to the loss of coupling between the turbine speed and the grid frequency [3]. Additionally, in the maximum power point tracking (MPPT) control mode, the WT cannot provide power support when the power grid frequency changes [4]. Therefore, with the increasing penetration of wind power, the problems of reduced system inertia and insufficient primary frequency regulation capability are becoming more and more obvious, which bring unfavorable factors to the grid frequency [5]. For this reason, the WT should have inertia response characteristics and primary frequency regulation capability such as synchronous turbines in order to achieve wind power friendly grid connection. To address this issue, the control methods currently used mainly include virtual inertia control and droop control. Virtual inertia control uses the system frequency rate of change as input to participate in frequency regulation and is mostly used to support transient frequencies. The concept of virtual inertia control was introduced earlier in [6], which makes full use of the high controllability and fast response of the WT output power, enabling the WT to simulate the inertia characteristics of synchronous turbines and release rotor kinetic energy to provide power support for the grid. The droop control enables the WT to have similar frequency droop characteristics as conventional turbines, to cope with system frequency fluctuations, to continuously support the system frequency, and to provide a strong support at the lowest point of the system frequency. In [7], the effect of droop control on system frequency was analyzed and the range of control parameters taken for different wind power penetration rates was obtained. The combined inertial control is a combination of droop control and virtual inertia control. In [8; 9; 10; 11], the improvement effect of combined inertial control on system frequency is investigated, and it is pointed out that combined inertial control plays an important role in reducing the frequency variation rate and maximum frequency deviation. In order to further optimize the effect of wind power participation in frequency regulation, many scholars have conducted further research on optimal control methods. In [12], a control strategy with variable droop parameters is presented to improve the adaptive capability of the control strategy, but the solution process is tedious, and the combination of virtual inertia control and droop control is not considered comprehensively. In [13], it simply involves a combined control strategy that considers virtual inertia control and droop control without specific analysis. In [14], PSO was used to optimize the virtual inertia parameters and droop control parameters, and wind speed adaptation analysis was performed in three scenes, which proved that the proposed method could significantly optimize the effect of frequency regulation. However, the method did not significantly reduce the frequency regulation time and the scenes were too few and lacked universality. Based on the aforementioned background, an improved combined inertial intelligent control of the wind turbine for temporary frequency support based on CAE and DNN is proposed in this paper. First, combined inertial intelligence is performed using PSO to obtain the optimal parameters and generate data sets. Then, CAE is introduced to extract features from the data sets.