## 4 EMD and DCNN Partial Discharge Type Recognition
### EMD and DCNN Partial Discharge Type Identification Analysis  
Partial discharge signal is a key feature to reflect the insulation state of power equipment, but it usually has the characteristics of non-linearity, non-stationarity, and strong noise interference. Traditional methods (such as the Fourier transform and wavelet analysis) have limitations in dealing with such signals. As an adaptive signal decomposition method,  
Figure 11: Joint looseness defect amplitude–pulse width–discharge times diagram. Figure 12: Flowchart of EMD–DCNN release type identification. empirical mode decomposition has the following two main advantages in dealing with partial discharge signals. First, traditional signal analysis methods (such as wavelet transform) need to set the basis function in advance, which is difficult to adapt to the complex time-varying characteristics of partial discharge signals. The EMD adaptively decomposes the signal into multiple intrinsic mode functions through a \"screening\" process, without any prior basis function constraints. This feature enables it to accurately capture the transient pulse waveform of the partial discharge signal. Second, partial discharge signals are often contaminated by background noise (such as white noise and periodic interference). EMD separates signal and noise through multi-scale decomposition. High-frequency IMFs (such as IMF1-IMF2) mainly contain noise components, while low-frequency IMFs (such as IMF3 and above) retain partial discharge characteristics. The network model DCNN is built by using the MatlabR2023a Deep Learning Toolbox, the ReLU function is selected as the activation function in the network. The recognition platform is a 3.0 GHz i7 processor, and the memory is 16 GB. To determine the parameters of the DCNN model, the partial discharge signal IMF waveform is used as input, and the identification targets are insulation scratch defect, bushing fouling defect, and joint loosening defect, respectively. This paper compares the recognition effect of the network when the learning rate is 0.01, 0.001, 0.0001, and 0.00001, respectively, as shown in Figure 13. When the learning rate is set to 0.01, although the network can quickly extract the initial features, the feature information is lost due to the fast convergence speed, and the recognition accuracy is low, indicating that the model cannot effectively capture features at high rates. By adjusting the learning rate to 0.001, the network shows progressive optimization characteristics: The recognition rate exceeds 80% when the iteration is 60 times, and the subsequent trend continues to rise. It is worth noting that when the learning rate is less than 0.001, although the model can still learn, the learning rate is significantly slower. To achieve the same recognition accuracy as the learning rate of 0.001, it takes more time to train. In addition, a learning rate that is too small can also easily cause overfitting problems. Therefore, considering the convergence speed and model stability, the learning rate of 0.001 becomes the optimal choice. The learning rate is set to 0.001, and the influence of the number of iterations on the recognition rate is analyzed. The recognition effect is shown in Figure 14. It can be seen from the recognition rate curve of the network that as the number of iterations increases, the recognition rate gradually increases and finally stabilizes at about 95%. When the  
Figure 13: The recognition rate of the network under different learning rates. number of iterations reaches 320, the recognition rate and loss value tend to be stable. This means that the performance of the model is relatively stable at this stage. If we continue to increase the number of iterations at this time, although it may bring an extremely small performance improvement, it will consume a lot of running time, which is not cost-effective from the perspective of the input-output ratio.