## 4 Application and Control Procedure  
Combined inertial intelligent control of wind power frequency regulation based on deep neural network is divided into three parts: acquiring data, extracting features, and predicting parameters. Feature extraction and learning of the original combined inertial control is performed to improve and optimize the control strategy. PSO is first applied to obtain combined inertial optimal control parameters in the simulation and combined with the scene data to generate the data sets. Then, CAE is used to extract data features from the data sets. Finally, DNN is used to learn the features so that the optimal combined inertial control parameters are quickly obtained when making decisions online. It is primarily categorized into two components: offline training and online decision-making.  
Offline training:  
Step 1: Define objective function and constraint conditions for PSO, and conduct time-domain simulation. Apply the PSO algorithm to obtain the optimal combination of combined inertial control parameters, and match it with the wind speed, wind power proportion, and load disturbance; use these three variables in corresponding scenarios to create data sets.  
Step 2: Pre-process the obtained data sets: CAE is used for feature training to extract the noise present in the original data and to improve the robustness of the DNN prediction model.  
Step 3: Normalize the obtained data and split it into training and validation sets. Allocate 90% of the original data for the training set and the remaining 10% for the validation set.  
Step 4: Conduct unsupervised greedy layer-wise pre-training. Utilize feature vectors obtained from training data through CAE training as input data, and apply the dropout technique to randomly deactivate some neurons, thereby reducing the connectivity among particular nodes, enhancing the generalization ability, and preventing overfitting. This process is iterated in succession and trained layer-wise to acquire the weight matrix \(W\) and bias vector \(B\) for the initialization of neural network.  
Step 5: Perform supervised fine-tuning to refine the initial network parameters. Specifically, in this study, the input weight matrix, hidden layer feature vector, and output weight matrix of the network are optimized by using the Adamax optimization algorithm.  
Step 6: Use the mean square error (MSE) as the evaluation metric of the model. If the MSE decreases, return back to Step 5 until the MSE stops decreasing. The training process of the network parameters ends when the MSE no longer decreases, and the trained model is saved.  
Online decision-making:  
After the offline training is completed, when the load disturbance event occurs again, it is possible to quickly match the scene data and use the trained network model to quickly obtain the optimal combined inertial control parameters to achieve the best combined inertial control and achieve rapid stabilization of frequency drops. The specific steps of online decision-making are as follows.  
Step 1: Collect the necessary system data for the specific scenario, including the above three variables, and create online data sets.  
Step 2: Normalize the acquired online data sets.  
Step 3: Feed the normalized data into the DNN model that was saved after offline training. Through this step, the optimal combined inertial control parameter combination will be swiftly and precisely acquired, forming an entire control strategy.  
Step 4: The entire frequency regulation strategy is input into the power grid system for online decision-making, achieving efficient and steady control of power system frequency.  
The specific procedure is shown in Figure 7.