Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied is higher than among different defects, so it is reasonable to use them as the basis for PD pattern recognition. The contrast indicates that the HOG method not only has the highest accuracy with the least requirement for pretreatment and training, but it also has robustness when the voltage applied changes. Consequently, this method has the universality for PD pattern recognition that is based on 3D PRPS graphs.

In this paper, an advanced wireless network called WMN was researched for the construction of UPIOT, especially from the perspective of power equipment. By introducing the fundamentals and analyzing the advantages of WMN, it proved that the WMN should have great application value in the communication system in Power Equipment of the UPIOT. By comparing ZigBee mesh and BLE mesh, it indicated that the WMN can be built by different technologies in relative application scenarios. By proposing a design of architecture with the application of WMN, it provided an innovative idea to construct the Power Equipment of the UPIOT and some theoretical supports for the subsequent research of the Ubiquitous Power Internet of Things.