A combined model based on improved information entropy and vague support vector machine (IVSVM) is introduced into transformer fault diagnosis using dissolved gas analysis in oil (DGA). The improved information entropy method is used to obtain the weights of each gas and to weight the raw data, and the processed training data and the corresponding fault types are inputted into the vague support vector machine (VSVM) model to obtain classifiers. Firstly, the training data are weighted by the improved information entropy method to discretise the original data from the mixed state for subsequent classifier training. Then, the vague set divides the events into true, false and unknown factors, which can optimise the sub‐interface of SVM and improve the accuracy of the boundary point classification. Finally, fault data from the literature and actual collections are selected for training and testing. By comparing with the widely used ratio method and artificial intelligence method, it can be concluded that the method described herein can effectively improve the accuracy of fault diagnosis. The result shows that this method has better applicability when facing actual fault type classification with higher data similarity.

Basin‐type insulator often has small cracks due to stress concentration. The current method cannot accurately reflect the stress condition of the insulator to find the stress concentration areas. To solve these problems, a method for detecting two‐dimensional plane stress ( δ ₁ and δ ₂ ) within different depth ranges in a basin‐type insulator is proposed based on critically refracted longitudinal (LCR) wave. First, the acoustoelastic equation characterising the relationship between the variation of LCR wave propagation time and the plane stress was derived. Next, the propagation characteristics of LCR wave in epoxy resin samples were investigated. Then, the stress distribution within different depth ranges of the insulator subjected to hydraulic load was measured using the proposed method, including direction ( θ ), δ ₁ and δ ₂ . The results show that the magnitude of the stress alone cannot accurately characterise the stress state. Points with equal distances to the centre have similar stress magnitudes, but their directions are not the same. With increasing depth, θ remains essentially unchanged at the same location, while δ ₁ and δ ₂ decrease, and the rate of decrease varies at different locations. Comparing the measured and simulated data, the results showed that they were in good agreement, and the maximum errors of stress value and θ were 0.69 MPa and 2.97°, respectively, which confirmed the feasibility and accuracy of the stress detection in the proposed method.

Partial Discharge (PD) measurement is one of the effective methods for assessing the internal insulation condition of power transformers in factories and substations. The pulse current signals generated by PD within transformer windings are significantly influenced by the winding structure during their propagation from the discharge source to the external measurement system. This influence may lead to misinterpretation of the insulation status, particularly in the analysis of PD measurement results. Such effects are closely related to the signal transmission path and distance and exhibit a strong correlation with the winding transfer function, manifesting as attenuation, distortion, or delay of the measured signals compared to the original PD waveforms. Therefore, it is essential to investigate the impact of the discharge path on the propagation characteristics of transformer windings and its effect on PD waveforms. This paper establishes a simplified distributed parameter model of a 180-turn single-winding multi-conductor transmission line using the finite element method and mathematical modeling, deriving the transfer functions between the winding head or winding end and various internal discharge positions. By injecting different types of PD waveforms collected in the laboratory at various discharge locations within the winding, the alterations of PD signals propagated to the winding head and winding end are simulated, and clustering analysis is performed on the propagated PD signals of different types.

For extremely large ultra-high-voltage (UHV) power transmission and distribution lines using hybrid reactive power compensation (HRPC), single-phase earth fault (1LG), three-phase ground fault (3LG), phase-to-phase fault (2LS), and two-phase grounding fault (2LG) often occurs. This study uses numerical emulations to examine the indicators of the interruption performance for the circuit breaker in case of different faults. Based on the equivalent-valued parameter circuit and the structure of a UHV demonstration project, formal expressions for the interruption performance of the circuit breaker are deduced, and the influence of such factors as the location of the fault and the degree of HRPC on the interruption performance of the 1LG, 2LG, 3LG, and 2LS is analyzed. Finally, the interruption performance of circuit breakers under different faults are compared. A quantitative evaluation of the influence of changes in the relevant indicators on the interruption performance of circuit breakers can provide a technical basis for choosing the appropriate design of the circuit breaker in light of the application as well as equipment development and assessment.

The conventional pulse current method is commonly used in AC or DC withstand voltage tests with partial discharge (PD) monitoring. Because of its relatively narrow measurement bandwidth, this method tends to ignore critical information in PD waveforms, making it difficult to eliminate pulse‐type interference under DC voltage conditions, where phase information is absent. To enhance interference rejection and achieve effective separation of multiple PD sources under DC voltage, an ultra‐wideband pulse current method was proposed. A detection impedance was developed to extend the upper limit of the measurement bandwidth to 50 MHz to retain PD waveform information. PD waveforms measured from three defect models using both the conventional pulse current method and the ultra‐wideband method were analysed in both time and frequency domains. The criterion function was employed to evaluate the difficulty level of classification of the PD waveforms obtained by the two measurement methods, and the results were validated using equivalent time–frequency (T–F) spectra. Field interference waveforms were introduced, and the density‐based spatial clustering of applications with noise (DBSCAN) clustering method was applied for unsupervised clustering of the PD waveforms and interference waveforms. The results demonstrate that the ultra‐wideband method can effectively separate multiple PD sources and eliminate interference sources under DC voltage conditions.