The design of high voltage and medium voltage AC and DC standoff insulators, and, in general, of insulation systems where surface and interface properties are predominant factors, is based on specifications and standards that rely upon long‐term experiences and lab testing. Surface insulating properties depend on electrical and thermal stresses, besides environmental factors, and the inception of extrinsic ageing phenomena, such as partial discharges, can be already a cause of reduced reliability and premature failure (for organic insulating materials). An innovative approach, based on field simulation, discharge modelling, and partial discharge inception measurements is presented by the authors, which can establish a solid basis to optimise insulation design for any type of supply voltage waveform. This method, the three‐leg approach, is applied here to a cable system under AC sinusoidal voltage, but it can work for any type of insulation system, such as bushings, switch gears, transformers, rotating machines, and power electronics boards.

Tri‐post insulators in gas‐insulated transmission line (GIL) are usually fabricated with high mass fraction of micron aluminum oxide (Al₂O₃), which will unavoidably settle under the action of gravity during the preparation process and seriously affect the uniformity. A model of filler sedimentation in epoxy resin composite materials was proposed based on the particle size analysis and Stokes' Law. Some scaled tri‐post insulators were prepared and tested by slicing. It is determined that the position of density concentration is the lower side of the two lower posts and the upper interface between the insulator and the conductor. The density ranges from 2.144 to 2.346 g/cm³. The dynamic fracture simulation model of insulator was established and it is found that the insulator fracture occurs at the interface of upper post/insert under radial load, which is verified by experiments. By comparing the influence of sprue position on the density distribution, it is found that the uniformity of insulators is increased by 13.7% by forward pouring compared with reverse pouring. This research develops an accurate method for simulating the filler sedimentation and the fracture process in epoxy‐based insulators, which is helpful for the improvement of mechanical reliability of GIL.

Automatically and accurately detecting the self‐blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open‐loop cognitive models to detect the self‐blast state of glass insulators, this study explores a mixed data augmentation‐based intelligent recognition method to detect the self‐blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high‐quality generative self‐blast samples of the glass insulator, and the non‐generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi‐scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi‐deformable convolutional network. Then, the extracted feature maps are transmitted to a two‐dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self‐blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self‐blast state of the glass insulator. Then, based on transfer learning and the established self‐optimising feedback mechanism for feature pyramid network, the self‐optimising adjustment and reconstruction of the feature map space with strong semantics and multi‐scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi‐channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.

The measurement of the surface charge on the insulating dielectrics is critical to estimate the insulation strength of electrical equipment in operation. Unavoidable interactions with the electromagnetic field, however, generally result in distortion measurement of active sensor. Thus, it is of great significance to investigate a passive sensor for monitoring the deposited charge. Here, a passive optical fibre electrostatic sensor is developed on the principle of Fabry–Perot interference to quantify the surface potential of the insulating dielectrics. The sensitivity of quantifying the electrostatic charge is explored in terms of the wavelength shift in the interference spectrum, and the linear correlation is 0.99. Meanwhile, the sensor can identify a negative quadratic trend with the increase of the surface roughness of the insulating dielectrics. The merits of the proposed sensor such as passive monitoring, bipolar electrostatic detection, and the excellent ability of anti‐electromagnetic interference are highlighted in the results. Hence, this sensor can serve as an effective tool to monitor the electrostatic charge in the fields of smart electrical equipment, fuel transportation, aerospace, integrate circuit etc.

In this paper we investigated SF₆ decomposition behaviour of typical insulation defects in gas‐solid insulated converter transformer valve‐side bushing under partial discharge (PD). Additionally, we compared the results with that of gas insulated switchgear (GIS). The results indicated that SF₆ decomposition behaviour varies with the insulation defects type. For metal protrusion defects, the proportion ranking of CO₂ and SO₂ can be related to whether the needle tip is wrapped with epoxy resin‐impregnated paper (ERIP). The proportion of carbon‐containing products is much higher under contamination defect than under other defects. Moreover, the proportion of carbon‐containing components is higher under flashover condition than under PD condition and is accompanied by the appearance of H₂S and CS₂. However, the yield of SO₂F₂ is always low. Compared to negative DC voltages, positive DC voltages have a higher proportion of carbon‐containing components, mainly in the form of more CO₂ and less SOF₂. Compared to the PD decomposition behaviour of typical insulation defects in GIS, when PD involves ERIP, the proportion of carbon‐containing products, especially CO₂, is significant higher, while the proportion of sulphur containing products, especially SO₂F₂, is lower.