Deep convolutional neural network for partial discharge monitoring system

Abstract

In the insulating material, the event of PDs takes place due to the air pockets, voids, or imperfections in such fields. In HV components like cables, the PD detection aids in ascertaining the insulating material condition. Consequently, the territory of PD estimation and conclusion is acknowledged as one of the most significant non-destructive methods for surveying the quality and specialized respectability of HV power mechanical assembly and links. In this work, a new data-driven model to recognize the condition of PD pulses of power cables is proposed with the aid of optimized Convolutional Neural Networks (CNNs). The two main stages of the developed framework are feature extraction and recognition. The data downloaded by VSB from the power cable is subjected to dimensionality reduction via Principal Component Analysis (PCA). Then, from these signals, the features are extracted with the aid of technical indicators like Rate of change (ROC), Relative Strength Index (RSI), Adaptive Moving Average (AMA), and Standard Deviation. Additionally, the features of the original power line data are also extracted. These features are put into an enhanced CNN as input. A new hybrid version known as the Combined Sealion-Swarm Optimization Algorithm (CS-SOA) optimizes the weight and activating function of CNN to increase the accuracy of the PD condition observation. From the result, it can be noticed that the accuracy of the proposed model, is 77.7%, 5.55%, and 3.335% higher than the existing models like SVM, CNN, and LSTM at TP= 80.