The research on intelligent diagnostic models for power transmission and transformation equipment has attracted increasing interest. In the design of practical diagnostic models, the supervised learning strategy is often used. This put forward higher requirements for the number of labeled data. However, in industrial applications, the cost of collecting a large number of relevant data and effectively labeling is high. At the same time, in the case of unknown faults, the lack of prior knowledge makes accurate classification difficult to achieve. This work designs a semi-supervised hybrid framework to diagnose the state of the converter transformer in terms of the labeled data which is limited. The framework consists of three main modules: multifeature graph generation, semi-supervised training, and soft voting decision. The graph generation module encodes the vibration signal into time, frequency, and energy graphs by calculating the Gramian angular summation fields (GASF). The semi-supervised learning module integrates unsupervised learning and supervised learning strategies trained by partial label information and data reconstruction errors. The decision-making module makes multifeature decisions through soft voting. The effectiveness of the hybrid framework is verified by comparative experiments. The experimental results show that the scheme can learn and diagnose intelligently under harsh datasets.

The winding current harmonic content of converter transformer in operation is rich. However, affected by harmonics, the radial vibration characteristics are quite different from the power transformer, structural loosening and deformation caused by winding vibration are more prominent. Therefore, in view of the influence of current harmonics on axial and radial vibrations, this study builds a 3D winding electromagnetic vibration finite element simulation model, analyses the components and contents of current harmonics, compares the leakage flux and vibration distribution under the fundamental, harmonic and superimposed currents. The correlation relationship between the harmonic components and axial and radial vibration characteristics is obtained, and the vibration measurement test of ±800 kV converter transformer in operation is carried out to verify the correctness of the analysis method. The results show that the winding radial vibration amplitude will exceed the axial vibration affected by harmonics. More importantly, under the superposition of the fundamental, 5th and 7th harmonic current, the vibration contribution of the converter transformer tank is mainly radial vibration, and the vibration dominant frequency is shifted from 100 Hz under the fundamental current to 400 Hz.

Tower grounding device provides the discharging channel for fault currents, ensuring the safe and reliable operation of transmission lines. However, the carbon steel tower grounding device is prone to corrosion, which affects the discharging performance and causes potential safety hazards. Existing evaluation methods are divided into subjective evaluation through excavating grounding device and objective evaluation through experimental detection. However, these methods suffer from poor accuracy in the evaluation results. In order to solve the above-mentioned problems, this paper proposed a comprehensive evaluation method for corrosivity of grounding device based on fuzzy theory, taking soil resistivity, PH value, water content and salt content as evaluation factors, and integrating subjective and objective weights based on game theory, which realizes complementary advantages of subjective and objective methods. Finally, a comprehensive evaluation algorithm for the grounding device corrosion is formed. Experiments are conducted to validate the effectiveness and accuracy of the proposed method.

As the capacity of power systems grows, the need for quick and precise short-circuit fault location becomes increasingly vital for ensuring the safe and continuous supply of power. In this article, we propose a fault location method that utilizes electromagnetic transient convolution (EMTC). We assess the performance of a naive EMTC implementation in multi-phase power lines by using frequency-dependent parameters in real fault simulation, while using constant parameters in pre-calculation. Our results show that the location error increases as the distance between the fault location and the measurement location increases. Therefore, we adopt the aerial mode transients after phase-mode transformation to perform the convolution, which reduces the influence of frequency-dependence and ground loss. We conduct numerical experiments in a 3-phase 300-km transmission line, a radial distribution network and IEEE 9-bus system under different fault conditions. Our results show that the proposed method achieves tolerable location errors and operates efficiently through direct convolution of the real fault-generated transient signals and the pre-stored calculated transient signals.

This research employs a high temporal resolution particle imaging velocimetry system to observe the entire bridging process of cellulose particles in insulating oil from a microscopic perspective. The analysis focuses on the particle dynamic behaviors that play a significant role in bridging and provided an in-depth elucidation of the bridging mechanism. By analyzing the motion characteristics of cellulose particles, mechanisms including charging, polarization, charge transfer, and inter-particle interactions were discussed. The space charge polarization effect of charged cellulose particles (SPCP) was discovered, leading to the development of a cellulose particle dynamic model that incorporates SPCP. This model provides a rational explanation for the observed dynamic behaviors of cellulose particles and holds the potential to become a fundamental theoretical in the field of liquid dielectric impurity particle contamination.

This study presents a novel method for measuring the collisional charge of micron-sized particles in insulating oil. Specific studies involves measuring the charge of micron carbon particles and cellulose particles in insulating oil and fitting the data to develop calculation formulas for collisional charge under various charging modes. The developed formulas provide crucial charge data to study impurity particle migration and aggregation in oil-immersed power equipment. The study also examines the impact of wall material, particle size, and electric field intensity on the collisional Charging Characteristics of particles. The collisional charge of particles is proportional to their surface area. As the electric field is strengthened, the collisional charge will tend to saturate. Furthermore, the polarity of the collisional charge of particles has been summarized. The measurement method proposed in this study has an error of approximately ±3.5% and performs well in measuring the collisional charge of micron-sized particles in liquid media.