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.

An improved variational mode decomposition (VMD) method is introduced into the mode recognition of low‐frequency oscillation of power system in this study. First, fast Fourier transform is used to determine the number of VMD components, and the approximate fitting formula of the best balancing parameter α is obtained by a large number of tests. The original signal is decomposed into several modes via improved VMD (IMVD) method. Then, Teager–Kaiser energy operator is applied on the fitting of each component to get the amplitude, frequency, and damping factor of it. By the constructed test signals, the proposed method is compared with the methods of non‐parametric VMD, empirical mode decomposition, total least‐squares estimation of signal parameters via rotational invariance techniques, and Prony on the performance of mode parameter identification. Results show that the IVMD method effectively overcomes the shortcomings of those methods mentioned above in dealing with mode mixing, noise sequence, and non‐stationary signals. Finally, the feasibility of the proposed method in extracting the low‐frequency oscillation mode parameters of power system is verified by the simulation signals of the IEEE two‐area four‐generator power system and the New England 39‐bus system.

When a single-phase-to-ground fault occurs in a power system, a secondary arc will normally be generated at the fault position after the fault phase has been interrupted. A detailed investigation of multiple-reignition features of secondary arcs on extra-high-voltage/ultra-high-voltage transmission lines is, therefore, very important. In this paper, an experimental setup for generating a secondary arc across a transmission-line insulator string is presented, and both the discharge waveforms and images are synchronously recorded. According to the properties of the discharge waveforms, the discharge process is divided into two stages and each stage is analyzed. The evolution of the volt-ampere characteristic and the resistance-current curve of the secondary arc is found to exhibit hysteresis during the discharge process. Multiple-reignition images of the secondary arcs are converted into binary images using image threshold processing. The arc length and breakdown voltage are discussed in association with the discharge images. Morphological changes in the secondary arc during a single discharge process are analyzed in terms of self-magnetic compression theory, and the extinction mechanism of the arc discharge is discussed on the basis of two theoretical approaches.

Transformer oil and insulation paper are subject to the combined effects of the electric field and temperature operations, which significantly impact insulation deterioration. This article designs a comparative test of the electro-thermal combined aging to obtain several characteristic parameters for the aging degree of oil-pressboard insulation. Molecular dynamics are used to establish a mixed molecular model of oil-pressboard insulation to simulate the pyrolysis reactions at high temperatures using the ReaxFF force field. The microscopic effects of the electric field on the aging of oil-pressboard insulation are studied. The rationale of the simulation model is verified from experimental results, and the trend of the reactants and products in the reaction process was considered to determine the influence mechanism of electric–thermal coupling on oil-pressboard insulation. The experimental results show that thermal stress remains the most important factor toward cracking in insulation paper. The acid value and furfural formation rate in oil increase, and the polymerization degree of the insulation paper decreases rapidly after the combined electric and thermal aging. In addition, the molecular dynamics simulations show that pyrolysis increases the bond energy, promotes repulsion between atoms, and leads to fractured bonds. The addition of the electric field increases the susceptibility of the chemical bonds in cellulose molecules to breakage. The morphology of the cellulose chain then changes, and the diffusion of small molecules intensifies. This study reveals the mechanism of insulation aging under electro-thermal coupling through combined macroscopic testing and molecular dynamics simulations.