Rail damage characteristics are crucial factors affecting the contact quality in electromagnetic launch, while interface current and temperature are critical parameters determining the contact state. To improve launch quality and guide equipment design, this work establishes an electromagnetic launch experimental and testing platform to analyze rail surface damage patterns. Based on the experimental results, simulation models for different contact states are constructed, and the interface electrothermal characteristic distribution is calculated. The results indicate that mechanical friction occurs initially between the armature and rail, followed by the formation of a liquid metal film under the action of high temperature, which not only reduces surface roughness but may also trigger gap discharge. The increased peak value of the pulse current and the number of launches exacerbate the damage. During the launch process, the current density and high-temperature areas are primarily concentrated at the rear end of the armature–rail contact surface and in regions with high curvature, with abrupt changes observed at phase transition points. Finally, based on the variation in contact resistance, optimization strategies are proposed for the front, middle, and rear segments of the rail.

UHV transmission lines are usually equipped with high-voltage shunt reactors. These reactors and the resulting degree of compensation affect the voltage, current, and other electrical characteristics of secondary arcs. In this paper, a low-voltage experimental simulation setup to produce secondary arcs in different compensation modes is established, and the morphological and electrical characteristics of recorded arc images and discharge waveforms are analyzed. For morphological characteristics, the degree of secondary arcs is compared for different compensation modes. For electrical characteristics, the arc waveform, volt–current characteristics, and zero-current time of secondary arcs are compared for different compensation modes. These results provide a foundation to determine the time of extinction of secondary arcs on overhead lines in the case of compensation, as well as technical support to develop effective arc suppression and extinction techniques.

This paper aims to increase the diagnosis accuracy of the fault classification of power transformers by introducing a new off-line hybrid model based on a combination subset of the et method (C-set) & modified fuzzy C-mean algorithm (MFCM) and the optimizable multiclass-SVM (MCSVM). The innovation in this paper is shown in terms of solving the predicaments of outliers, boundary proportion, and unequal data existing in both traditional and intelligence models. Taking into consideration the closeness of dissolved gas analysis (DGA) data, the C-set method is implemented to subset the DGA data samples based on their type of faults within unrepeated subsets. Then, the MFCM is used for removing outliers from DGA samples by combining highly similar data for every subset within the same cluster to obtain the optimized training data (OTD) set. It is also used to minimize dimensionality of DGA samples and the uncertainty of transformer condition monitoring. After that, the optimized MCSVM is trained by using the (OTD). The proposed model diagnosis accuracy is 93.3%. The obtained results indicate that our model significantly improves the fault identification accuracy in power transformers when compared with other conventional and intelligence models.