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Transformer bushing is one of the key equipment of transmission system, and its performance directly affects the stability and safety of transmission system. This paper is aimed at studying the safety detection of electrical properties of aramid shells with molecular dynamics and deep learning algorithms. The bushing needs to withstand AC voltage, DC voltage, and polarity reversal voltage during operation. The complexity of operating conditions leads to the improvement of bushing requirements for insulation performance, and bushing accident is a common type of transformer accident, accounting for 30% of the total number of transformer accidents. Therefore, it is necessary to detect the electrical performance and safety of the bushing to ensure the safe and stable operation of the power system. Aramid casing is a kind of casing with many advantages, such as high strength, high tensile breaking force, high stability, and high temperature resistance. Molecular dynamics is helpful to deeply analyze the micro mechanism of various complex phenomena, so as to explain the relationship between material microstructure and macroproperties, so it is very helpful to analyze the structure and properties of aramid casing. Deep learning is an important research direction in the field of machine learning. It can extract important features and simplify casing analysis steps. In this paper, an electrical performance test system of aramid casing is designed. It is proved that the reliability of casing is generally greater than 1 and the reliability is high. The current performance of the bushing is tested. 400 A is a dividing point, and no matter how large the current is, the maximum temperature is no more than 130°, which proves that the current performance of the bushing is stable and the temperature resistance is good. Finally, the radial field strength distribution of bushing capacitor core under different initial moisture content is tested, and it is concluded that the moisture can not be greater than 6%.

Electrical Performance Safety Detection of Aramid Casing Based on Molecular Dynamics and Deep Learning Algorithm