Partial Discharge Type Identification of 10 kV T-Type Terminal Based on Empirical Mode Decomposition and Deep Convolution Neural Network

As a relatively weak part of cable insulation, T-type cable terminals will have insulation defects due to process, installation, and other problems, resulting in partial discharge. Therefore, this paper uses Deep Convolution Neural Network (DCNN) and Empirical Mode Decomposition (EMD) to identify the partial discharge type of a 10 kV T-type cable terminal. This method uses the partial discharge experimental platform of the T-type cable terminal to collect the partial discharge signal. After the original signal that is difficult to identify is decomposed by EMD, a series of intrinsic mode components (IMFs) that are easy to locate are obtained. The deep learning network model is used to identify the defect type of the IMF signal. The results show that the overall defect recognition rate of this method reaches 95.3%. Compared with the traditional random forest algorithm (RF), the 10 kV T-type terminal partial discharge type recognition method based on EMD–DCNN is considered in this paper. The recognition accuracy of the main insulation scratch, bushing fouling, and joint loosening defects is higher than that of the traditional mechanical learning algorithm, RF, indicating that the method adopted in this paper can more effectively and accurately identify the defect type.