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By correcting the photon area factor (PAF) to suit the structure of the spacer and redefining the effective ionization region of the process of negative corona onset, a physical model for the calculation of COG of AC spacers is established. The validity and practicability of the established physical model are validated by the corona onset test results of spacers in both low‐altitude and high‐altitude areas. The numerical results pertaining to PAF and effective ionization region are consistent with the theoretical analysis. With the established physical model as a basis, the COG of the spacer is calculated under different geometrical structure parameters and ambient conditions. The results imply that changing the curvature of the spacer edge has a more significant impact on the COG of the AC spacer than changing the curvature of the spacer end and thickness H. The decrease in the length of the ionization region and the rapid attenuation of spatial electric field intensity of the spacer with smaller geometric parameters lead to an increase in the COG. [ABSTRACT FROM AUTHOR]

The high‐quality 3D model is the key to digital twin and visualization. Here, the neural radiation field (NeRF) is applied to greatly improve the speed and accuracy of 3D reconstruction of large‐scale substation equipment. The incremental SFM algorithm is used to accurately position the camera in open scenes, and the frame extraction experiment indicates that the single angle increment should not be greater than 8°. The reconstruction test is carried out on the power transformer and high‐voltage experimental equipment following the circular shooting track for instant‐NGP (a variant of NeRF). Compared with the traditional 3D reconstruction method, the speed of reconstruction is accelerated by 23–55 times, and the fidelity is improved by 2–3 times. Also, the deployment scheme of NeRF in the digital twin technology of large‐scale substation equipment is proposed. The experimental results verify the effectiveness and feasibility of NeRF in practical application. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) localization is critical to ensure the safe operation of power equipment. In this paper, an optimization scheme is developed for PD positioning of external insulation equipment. The optimization scheme is based on an eight‐element cross sensor array to receive the ultrasonic signal generated by the PD, and uses the highly accurate 2‐D multiple signal classification (2‐D MUSIC) algorithm to locate the signal. Simulated discharge signal is adopted to test the optimization scheme, and the results show that the positioning error is less than 0.61° when the signal‐to‐noise ratio (SNR) is above −5 dB. In addition, three discharge models are tested at various locations. The average azimuth errors are 0.9°, 1.9°, and 1.55° for the needle‐plate, cone‐plate, and ball‐plate discharge models, respectively, and the average pitch angle errors are 0.75°, 2.1°, and 1.4°, respectively. These show that the needle‐plate model has the best positioning effect. Simulations and experiments are also carried out on the double discharge source scenario, and the error is within 2.2°. A PD location visualization equipment based on the proposed optimization scheme is developed and applied on‐site. The equipment is capable of satisfying the requirements of operation and maintenance. [ABSTRACT FROM AUTHOR]

Saturable reactor is the key equipment in converter valves; it is important to evaluate the core condition. In this paper, a recognition model of saturable reactor core loosening based on variational mode decomposition (VMD)‐symmetrized dot pattern (SDP) feature fusion images and scale‐invariant feature transform (SIFT) was proposed. Firstly, the saturable reactor vibration test under high frequency pulse excitation was carried out, and the vibration signals in different core loosening degrees were collected. Secondly, the VMD algorithm was used to decompose the broadband vibration signal into multiple narrowband functions, which were used to reflect the characteristics of each frequency band. Thirdly, each function and the original signal were transformed by SDP, the generated spiral arms were fused into a new image, and the typical templates in different loosening degrees were selected. Finally, the improved SIFT algorithm was used to obtain the matching results between test sets and templates. The results show that the recognition accuracy of the proposed model for core loosening is 97.5%, which is better than traditional algorithms. It can find the core loosening defect early and avoid further failures such as water pipe break and discharge, which can provide an important basis for saturable reactor monitoring. [ABSTRACT FROM AUTHOR]

Solar‐blind ultraviolet (UV) discharge detection based on UV signals in the solar‐blind band from 240 to 280 nm is an effective non‐contact discharge detection method for electrical equipment, but there is still a problem that the detection results of different types of UV imagers are not comparable. To this end, by using standard light source and MODTRAN model, the relationship between the facular area and the radiant flux received by UV imagers was investigated by establishing a radiation transmission model for UV detection under different environmental conditions. Through analysis, it was found that the influence of humidity on the transmittance of the solar‐blind band in atmosphere can be neglected, and the transmittance is slightly affected by atmospheric pressure, temperature, and detection distance under conditions suitable for UV detection. Moreover, a consistency formula for UV detection results was proposed, thus forming a consistency method for detection results suitable for various types of UV imager. Finally, the feasibility of the consistency method was verified by UV discharge detection tests with a rod‐plate model and the absolute irradiance at discharge points of the rod‐plate model measured by a grating spectrometer. The consistency method is conducive to the promotion of UV discharge detection technology. [ABSTRACT FROM AUTHOR]

An insulation board is an important insulator used between damping capacitors in a converter valve. The potential creeping discharge phenomenon on the insulation board will affect the insulation between the damping capacitors and even the safe operation of the converter valve; therefore, a modified chaos particle swarm optimization multiple signal classification (MCSPO‐MUSIC) localization algorithm based on a sparse array was proposed. The performance of the localization algorithm and the sparse array was analyzed by MATLAB simulation, and a test platform was established to detect the insulation board discharge position localization. The simulation results showed that the calculation time of this algorithm is about 1.5 s, which is an order of magnitude less than traditional MUSIC algorithm, and it is found that when the sparsity of the 4 × 4 array is 4 (the sparse array elements are 5, 9, 14 and 15), the localization accuracy remains high. Ten groups of experimental data were put into the MCSPO‐MUSIC algorithm; the root mean square errors (RMSE) of the localization errors are 1.91° (non‐sparse array) and 3.12° (sparse array), respectively. Finally, the blind source separation algorithm was used to remove the field noise, which verifies the algorithm and sparse concept accurate and economical in practical application. [ABSTRACT FROM AUTHOR]

For an alternating‐current (AC) shielding ball with a large radius of curvature, a three‐dimensional (3‐d) physical model for the corona onset voltage (COV) of the large‐diameter shielding ball under different ambient conditions was established according to numerical calculation in concert with the theory governing gas discharge. At first, distribution of the spatial 3‐d electric field around the shielding ball was calculated by using the ring charge simulation method. Then, a calculation criterion suitable for the COV of the shielding ball was derived and the area factor of the geometric absorption function for photons applicable to such structures was proposed in detail based on the theory of secondary photoelectron emission. Moreover, tests on COVs of shielding balls with different diameters were conducted in an extra‐high voltage (EHV) AC test‐base. The test results show that the model was found to show a favourable effect in calculating the COV of a large‐diameter shielding ball under different ambient conditions. Finally, in combination with the thickness of the ionosphere and effective ionization coefficient, the influence and mechanism of action of various parameters (such as atmospheric pressure, humidity, and temperature) on the COV of the shielding ball were investigated. [ABSTRACT FROM AUTHOR]

Time‐domain reflectometry applied for power cable diagnostics employs transmission line parameters to determine signal shape and delay. This paper proposes to utilize pulse reflection measurement to evaluate the symmetry of a three‐core power cable as part of production quality assessment. For a rotation‐symmetric three‐core power cable, transmission line parameters can be obtained analytically, and with modal analysis propagation channels can be decoupled. Asymmetry caused by inaccurate core positioning complicates the decoupling process. This paper utilizes the "field‐circuit" approach for the analysis. The finite element method is used to obtain the electromagnetic field distribution for two types of asymmetry introduced in a cable design. Modal analysis results are validated by considering asymmetry as a perturbation of the symmetric cable design. The influence of asymmetry on the time‐domain pulse response is simulated. Deviation from a symmetric configuration is observable, in particular in terms of propagation velocity, which can be employed to assess cable manufacturing accuracy. [ABSTRACT FROM AUTHOR]

Based on the analysis of the principle and structure of the convolutional neural network (CNN) model in a deep learning theory system, an intelligent method for judging the flashover of a porcelain insulator with ultraviolet discharge is proposed. In this method, the porcelain insulator chip was subjected to power frequency flashover testing, and the ultraviolet spectra of different discharge states without discharge, weak coronal discharge, and strong spark discharge were captured by FILIN UV imager. The Alexnet deep convolution neural network model was used to predict the discharge state of the UV spectrum for classification training and identification assessment. The new method doesn't use UV imaging to detect flashover warnings. It is necessary to extract the characteristics of the UV spectra and leakage current parameters manually. The multi‐layer combination of UV imaging method with end‐to‐end autonomous learning with deep learning training and the adopted test method provided classification identification, through a large number of UV images in the deep CNN training. This allowed flashover evaluation of abstract feature parameters of the independent extraction. The results show that this method has the advantages of high accuracy, and provides a new idea for the intelligent detection of UV flashover. [ABSTRACT FROM AUTHOR]