Deep convolutional neural network for partial discharge monitoring system

•Proposed a novel data-driven approach to recognize the condition PD pulses of power cables is proposed with the aid of the optimized CNNs.•The feature extraction and recognition are two major phases deployed in this research work. The data collected by VSB from the power cable is subjected for proposed PCA based dimensionality reduction.•Then, from these signals, the ROC, RSI, AMA and standard deviation based technical indicator features were extracted. The features of the original power line data were extracted in addition and all these features were together fed as input to optimized CNN. In the CNN, the weight and activation function were optimized via CS-SOA.•This evaluation was done by varying the training rate and the noise level. Here, the performance of the proposed model is learned for every variation in the training rate (TP), say 60%, 70%, 80% and 90%, respectively. The TP evaluation is done to comprehend the performance level improvement of the presented work over the traditional works like SVM, CNN and LSTM. Then, the performance of the presented work is compared over the existing works in terms of noise level. Here, during the testing phase, a white Gaussian noise is implied to the collected original signal and the performance of proposed work is analysed under different measures. This evaluation is done by varying the noise from 0.1, 0.2, 0.3, 0.4 and 0.5, respectively.