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With the construction and promotion of the Ubiquitous Power Internet of Things (UPIoT), it is an increasingly urgent challenge to comprehensively improve the recognition accuracy of the gas-insulated switchgear (GIS) partial discharge (PD), and to incorporate the model into UPIoT intelligent terminals supported by edge computing in embedded systems. Therefore, this paper proposes a novel MobileNets convolutional neural network (MCNN) model to identify the GIS PD patterns. We first construct the PD pattern recognition classification datasets by means of experiments and FDTD simulation, and also preprocess images via binarization processing. After constructing the MCNN model, depthwise separable convolutions and an inverse residual structure are adopted to deal with the vanishing gradient of the deep convolutional neural network (DCNN) in the GIS PD pattern recognition process. Then, through the graphics standardization process, the MCNN model is trained and tested. The whole training process is visualized by Tensorboard. Compared with other deep learning models and traditional machine learning methods, MCNN particularly stands out in recognition accuracy and time consumption with a 96.5% overall recognition rate and merely 7.3 seconds in training time. This research explores how to optimize the model by improving the recognition accuracy, and by reducing its computing load, storage space and energy consumption for better incorporation into intelligent terminals in the UPIoT context.

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A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application