As an onsite condition monitoring method, infrared inspection can help to discover and analyze abnormal temperature increases in power equipment. To improve the efficiency of onsite diagnosis of insulators in substations, this paper proposes an automatic diagnosis method using instance segmentation and temperature analysis of infrared insulator images. To develop this method, thousands of infrared images from field inspection databases were collected to establish an annotated data set of insulator images. With the aid of Mask R-CNN it was possible to extract multiple insulators automatically in the infrared images. Transfer learning as well as the dynamic learning rate algorithm were then employed to realize the training process of Mask R-CNN with the annotated image data set. The result of the testing experiment showed that the mAP of the model is 0.77 and the FPS is 5.07, which indicated great identification accuracy and computing speed of the proposed model. Next, function fitting was realized to extract the temperature distribution of each insulator. Finally, to evaluate the condition of each insulator, rules, which are based on the related standards, were established using machine language. This is the first time that the machine could independently realize fault analysis of multiple insulators in the infrared images, which is a great attempt to adapt the development of the Internet of Things and the tendency of predictive maintenance. Moreover, because of the universality of the model algorithm used, automatic infrared fault diagnosis for other power equipment could also be performed in a similar manner, which has significant potential applicability in the area of power equipment diagnosis.

Corona discharge could be a serious hazard to the outdoor insulation of a high-voltage (HV) system and also is an important detection issue for insulation status diagnosis. In this study, a double-helix-ultrasonic-array (DHUA) sensor was developed for corona positioning, which showed a good sensitivity to the ultrasonic signal originating from corona discharge and a satisfied directional response in static beam patterns. Based on the matting-based image fusion method, the acoustic intensity matrix was fused to a visual scene in real-time. This ultrasonic visualization technology was then evaluated with a simulation test and performed in a real air insulated substation (AIS), and showed a satisfied performance in terms of sensitivity and spatial location resolution.

Frequency domain spectroscopy is considered to be a promising and novel method for the assessment of the insulation condition of power equipment. This work has practical significance as it explains the microscopic mechanism of this method in a wide temperature range and further establishes its quantitative model. To achieve this, in the present paper, we select oil-impregnated paper—one of the most common insulation materials for power equipment with a complex microstructure—as a test sample, deduce a formula based on the relationship between the real and imaginary parts of the complex permittivity to extract the spectra of independent dielectric processes and measure the frequency domain spectra of oil-impregnated paper under different temperatures, as well as its thermally stimulated depolarization current with a series of bias voltages. The analysis results reveal that oil-impregnated paper’s frequency domain spectra in a wide temperature range are mainly determined by dielectric processes whose generation mechanisms are low-frequency dispersion, DC conduction, electrode relaxation, interfacial relaxation and dipole relaxation, respectively. Moreover, due to the different thermal properties of charge motions, the macroscopic characteristics and microscopic generation mechanisms of both spectra vary significantly with the sample’s temperature. After verifying the generation mechanisms of the spectra in high, middle and low-temperature ranges, function models for those spectra with clear physical meanings are established separately, providing sufficient physical parameters to carry out insulation assessment.

Frequency response analysis is widely used to diagnose transformer winding deformation faults due to its high sensitivity, strong anti-interference capability, and equipment portability, but the results of frequency response analysis can be affected by insulation aging and moisture in the transformer, leading to errors in the diagnosis of winding deformation faults. Currently, there is no effective method to prevent such errors. This paper focuses on optimizing the criterion for diagnosing winding deformations when insulation aging and moisture are present. First, the winding frequency response curves of oil-paper insulation were determined by combining insulation aging and moisture tests of the oil-paper insulation with frequency response simulations of the transformer winding. Next, the winding deformation criterion predicting the likelihood and extent of errors diagnosing transformer winding deformations due to the insulation aging and moisture content is discussed. Finally, the corresponding criterion optimization method is proposed. The corresponding results show that insulation aging and moisture can lead to errors when using the correlation coefficient R criterion to diagnose the transformer winding deformations. Moreover, the possibility of winding deformation errors caused by the change of insulation state can be reduced by introducing the corresponding auxiliary criterion through comparing the capacitance change rate based on the frequency response method and that based on the dielectric spectrum method.

Due to the compact design and complex structure of the transformer, during the installation the transformer may be defective due to improper operation. During the transformer online working, three kinds of field: heat, electricity and mechanical stress will act on the transformer for long time, water could invade the weak links. In oil-immersed paper system, little amount of water lead to the failure of the whole insulation. In order to explore the effect of water content on transformer bushing, dielectric response method was used to simulate the dielectric spectra of bushing under different water content based on single damp oil-immersed paper and combined with the actual structure of bushing. The paper used Kramers-Kroning equation to analyze the experimental and simulation results, and conclude two points as follows: The loss component is positively correlated with water content, meanwhile the change of water content in bushing will not affect the linear relationship of conductance in frequency domain spectra. In the experiment, it is found that with the increase of water content, there will appear an obvious loss peak. This loss peak is due to the polarization caused by water intrusion in the oil-immersed paper system.