Recognition of partial discharge (PD) patterns in gas insulated switchgear (GIS), as a basis of fault diagnosis, provides essential information for the condition assessment of GIS. However, traditional recognition methods are limited to handcrafted feature extraction or are extremely sensitive to the quantity and quality of datasets. Therefore, this article proposes a knowledge-driven algorithm, composed of the feature space and the knowledge space, to automatically extract PD features and improve the performance on noised, insufficient, and imbalanced datasets. First, the deep residual network (ResNet) in feature space extracts features from raw signals. Second, in knowledge space, the graph convolutional network (GCN) extracts additional information from the knowledge graph, which compensates for the missing information of original datasets. Finally, the algorithm recognizes patterns by ranking similarities between feature vectors and knowledge vectors. Verified by the comparison experiment, the proposed algorithm outperforms traditional methods with the accuracy of 99.58% on the experimental dataset and 95.67% on the online-detected dataset. Moreover, the accuracy of the proposed algorithm achieves 88.79% and 70.37% on noised and insufficient datasets, respectively, while the F-measure is higher than those of the comparison methods by 12.95% $\sim$ 18.69% on imbalanced datasets.

Gas insulated switchgear (GIS) in service may tolerate large temperature changes, which leads to uncertainty in the evaluation of the partial discharge (PD) signal. To clarify the influence of temperature on the discharge signals of GIS insulation void defects, this paper proposes a multiscale fusion simulation method and used experiments to verify it. This method combines the streamer simulation at the micro level and the circuit simulation model at the macro level, and a more accurate simulation signal of GIS insulation void PDs under the influence of temperature can be obtained. Experiments have also been carried out, and a PDs detection experiment platform that can be set in different temperature environments was built. The discharge signals at different temperatures were obtained follow IEC60270. Finally, by combining analyses of simulation and experimental data, the law of the influence of temperature on the PD signal of GIS insulation voids was summarized, and the mechanism of the influence was analyzed. The results showed that the multiscale fusion simulation method of GIS insulation void PD signals proposed in this paper is consistent with the experimental results, which can provide a reference for the digital twin model of power equipment status.

To monitor the insulation deterioration of power equipment and realize prompt fault warning systems in air-insulated substations, in this study, we propose a multiple signal classification (MUSIC) algorithm-based partial discharge (PD) localization method with an angle of arrival (AOA) and ultrahigh frequency (UHF)-received signal strength indicator (RSSI). Compared with traditional UHF time-difference-based techniques, this RSSI-based AOA localization method is a more economical solution. In addition, by comparing the measured RSSI vector to a prebuilt reliable reference data set, the MUSIC method can effectively locate the direction of the PD source with high accuracy. Compared with the method that directly determines the smallest RSSI values by several sensors, this method can accomplish localization by fewer sensors without impeding accuracy. Furthermore, the interpolation method was adopted to improve the precision of the relationship curve of AOA/RSSI, which it did with a limited number of sensors. Laboratory tests were conducted to verify the accuracy of the proposed method, and most of the localization errors were less than 1°, which indicates its potential application in the prompt identification of faults regarding the insulation deterioration of power equipment in substations.

The severity evaluation of UHF signals of partial discharge in GIS is helpful to formulate a maintenance strategy in time, and improve the reliability of power system operation. Most of the evaluation methods are based on structured data, which cannot fully characterize the state of the equipment. In this paper, a method for evaluating the severity of UHF signals of partial discharge in GIS based on semantic analysis is presented. It comprehensively considers the influence of structured data and unstructured text data on the state of equipment, including measured data on partial discharge, relevant information of defects, and equipment operating parameters. Firstly, the severity of UHF signals of partial discharge is defined. According to the on-site detection of substations, a data set containing equipment detection reports and PRPS data is established. Aiming at semantic analysis, Word Embedding is used to associate and encode textual information to reduce the subjectivity of the encoding. Measured data on partial discharge is also combined and analyzed for evaluation. The method proposed is used for case analysis and compared with other methods. The results show that the comprehensive utilization of structured data and unstructured text data can reflect the state of equipment more comprehensively and truthfully, and improve the accuracy of the results.

Temperature is an important environmental factor during the operation of gas‐insulated switchgear (GIS), affecting the evaluation results of the GIS equipment to increase the risk of the power system. However, the influence of temperature on the partial discharge detection signal of GIS is still unclear. Aimed at the common void defects in GIS, the law of change on the number of ultrahigh‐frequency (UHF) pulses, the UHF amplitude, the characteristic value of the UHF map, and the maximum apparent charge of a single pulse with temperature are obtained using the UHF method and IEC60270, and corresponding theoretical analysis is carried out. The results show that an increase in temperature leads to a decrease in the void discharge delay time, causing an increase in UHF pulses and a decrease in the apparent charge of a single discharge pulse in the experiment. The increase of temperature makes the void discharge current rise quickly so that the induced UHF amplitude increases. In the range of 40–70°C, the maximum pulse amplitude increases by approximately 30% for every 10°C increase, and the average pulse amplitude increases by approximately 12%. The result of UHF signals affected by temperature obtained in this study has research significance for the realisation of a comprehensive evaluation of the insulation state of GIS equipment considering temperature.

The tip size of metal protrusions is an important factor affecting corona discharge, since it influences the electric filed, the ionization range and strength around the protrusion defect. Most existing studies on the influence of protrusion tip size on corona discharge are implemented by experimental methods. However, these studies are difficult to obtain the microphysical process of charged particles, which is related to the generation of current pulses and electromagnetic (EM) waves. In this paper, a hydrodynamic simulation model is proposed to study the effect of protrusion tip size on the microphysical process and current pulses of negative corona discharge. A mapping relationship between microscopic behaviors of charged particles and current pulses is established by this simulation method. Based on the comparison of microphysical quantities, the effect of defect tip size on the detailed parameters of current pulses is clearly explained. It shows that with the increase of tip size under a constant voltage, all the five parameters of current pluses increase. The rising time is related to the effective ionization coefficient, while the falling time is dependent on the velocities of positive and negative ions. The experiment and simulation results coincide in current waveforms and all five parameters.