A 1D CNN-based method for partial discharge pattern recognition achieves higher accuracy and lower complexity than 2D CNNs.
Big data platforms and centers are ubiquitous today where a large amount of unstructured data on site such as images is accumulated. For structured data, partial discharge pattern recognition method has been extensively studied, whereas traditional methods can not be directly applied to unstructured data. To this end, a time-domain waveform pattern recognition method based on one-dimensional convolutional neural network (CNN) is proposed. Image processing techniques are applied to obtain one-dimensional characteristics of the waveform. Based on deep learning, the network is constructed for pattern recognition straight forwardly. Through on site detection and simulation experiments, image data sets of five partial discharge defects are established and comparative experiments are conducted. Experimental results show that the proposed method can successfully perform pattern recognition with applications in work of data mining and data utilization. Under the same complexity, it is also with higher accuracy comparing to two-dimensional CNN. Furthermore, the method autonomously extrapolates features without manual extraction, which achieves low experimental complexity and robustness simultaneously.
Wan et al. (Sat,) studied this question.