Digital Twin-Driven Hot Spot Capture for Power Transformer Interturn Short-Circuit Fault Diagnosis Based on Microthermal Field

As one of the most common types of internal faults in power transformer, interturn short-circuit faults can lead to a rise in temperature and damage the lifetime of the transformer. However, it is challenging to accurately localize fault in time with the existing techniques. Digital twin (DT), with its capabilities of integrating and synchronizing the virtual and physical worlds, is an essential tool for accurately monitoring the internal state and health of transformers. It not only enables the timely detection and resolution of issues but also improves maintenance efficiency and reduces costs. In this article, a multisensor data fusion method for capturing hot spot based on microthermal field DT is proposed to improve the fault localization accuracy and timeliness. First, a DT model of temperature distribution based on microthermal field is constructed by finite element analysis (FEA). The raw temperature signals in multisensors installed in the oil channel are collected by value game. Second, the key information for monitoring the spatiotemporal variation is obtained by conjoint analysis. A data fusion method of capturing hot spot temperature is proposed. Finally, through feature extraction, a diagnosis method based on Bayesian inference is developed for different degrees of faults according to different oil flow line symmetry characteristics. The simulations and practical results all show that the proposed method can not only ensures the accuracy of the localization but also improves the timeliness of the detection. This work provides a novel solution for the localization of interturn short-circuit fault and is of constructive idea for solving the fault diagnosis problem by physical fields DT.