Generator Stator Partial Discharge Pattern Recognition Based on PRPD-Grabcut and DSC-GoogLeNet Deep Learning
Yuzhu Chen, Xiaosheng Peng, Hongyu Wang, Jin Zhou, Yue Zhang, Zhiming Liang
Abstract
Partial discharge (PD) pattern recognition of typical defects from generator stator bars is the basis of generator condition monitoring and fault diagnosis. A PD pattern recognition method for generator stator bars based on a phase-resolved PD graph cut (PRPD-Grabcut) and depthwise separable convolution GoogLeNet (DSC-GoogLeNet) deep learning neural network is proposed in this article. First, five typical defects are designed on stator bars in the laboratory. A long-term high-voltage test is carried out, and 37500 phase-resolved PD (PRPD) original graphs are obtained. Second, a PRPD-Grabcut method based on image segmentation is proposed, which is designed to extract the key components of the PRPD map. Finally, a DSC-GoogLeNet-based PD pattern recognition method is proposed, which combines the GoogLeNet inception module and depthwise separable convolution. Experimental results show that the PRPD-Grabcut contributes to certain improvements in the recognition accuracy and training efficiency of the neural network. The DSC-GoogLeNet shows superiority in recognition accuracy, cross-entropy loss, and training time compared to a variety of the existing lightweight neural networks. In addition, compared with other networks, the DSC-GoogLeNet greatly improves the recognition accuracy of PD types with similar but different risk levels for stator insulation.