Fault analysis of oil-immersed transformer based on digital twin technology

Abstract

Oil-immersed transformer is one of the important equipment to support the safe and stable operation of power system. At the same time, in the process of transformer operation, transformer failure is inevitable, this kind of failure belongs to multiple latent faults, which seriously endangers the safe operation of the transformer. Therefore, the maintenance of the transformer has played a crucial role. A fault detection method based on digital twin model is proposed to solve the problem that the internal dynamic characteristics of oil-immersed transformer are difficult to extract, and the fault detection error is large due to noise interference. The dynamic model of oil-immersed transformer is constructed, the main cause of failure is analysed, the failure time of internal insulator is taken as a linear index, and the linear correlation between failure probability and failure time is solved by Weibull distribution function. The fitting function between transformer fault residence time, operating days and fault probability variance is established, and the fitting value obtained is used as the initial data reference of the fault detection model. The digital twin model is used to establish the dynamic fault probability calculation function, and the on-site data is substituted to calculate the fault detection threshold, and then the data is compared to complete the detection.