## 1 Introduction  
As the core equipment in the power system, the regular operation of the power transformer is crucial to the stability and security of the power grid. However, the remanence of the transformer core significantly impacts its operational performance. Once remanence is generated, it does not disappear automatically [1]. The presence of remanence causes rapid magnetic saturation of the core and generates an excitation intrush current, the peak of which can reach six to eight times the rated current [2], seriously affecting the safe and stable operation of the transformer and the power grid. For example, in March 2016, the excitation inrush current generated when the No. 1 main transformer at Guandu Station in Henan Province was put into operation caused periodic distortion of the 500 kV bus voltage at the nearby Zhongzhou converter station, resulting in DC power fluctuations [3]. Similarly, in August 2015, the no-load closing of the 500 kV No. 4 booster transformer at Suizhong Power Plant generated an excitation inrush current, leading to multiple commutation failures in the Kaolin DC project [4]. It is evident that the inrush current threatens the mechanical stability and dielectric strength of the transformer winding and disrupts the regular operation of the power system. Therefore, accurate measurement of remanence is an important task to prevent the inrush current from endangering the safety of the power grid. Currently, the measurement methods for core remanence in power transformers primarily include empirical estimation of remanence, calculation methods based on core magnetization models, voltage integration methods, remanence measurement methods based on the excitation inrush current, and residual magnetic field measurement methods based on leakage. The empirical estimation of remanence relies on prior operating experience, resulting in significant errors and an inability to predict the direction of remanence. The remanence magnetism calculation method based on the core magnetization model allows for the estimation of remanence without transformer operation, but the magnetization models differ among transformers, making establishment challenging and prone to large errors. The voltage integration method is the most widely employed for measuring remanence, yet it also exhibits substantial errors [5]. The remanence measurement method based on excitation inrush current is cumbersome to operate but significantly enhances measurement accuracy; however, it remains restricted to simulation and analysis, with ongoing challenges in practical application. The residual magnetic flux measurement method based on transformer flux leakage is currently the most accurate method available, but it is still confined to the simulation stage, and there are unresolved issues concerning the establishment of the theoretical model for three-phase transformers [6]. Researchers have proposed using artificial intelligence to solve the important task of predicting the magnetic properties of permanent magnets based on rare earth metal alloys. The author studied the effectiveness of eight machine learning methods in predicting the magnetic remanence of Sm-Co magnets. The results indicate that only machine learning ensemble methods can provide sufficient predictive models, with the random forest algorithm achieving the highest prediction accuracy. However, current artificial intelligence methods are still limited to measuring the magnetic properties of specific materials, and there is no measurement and demagnetization method for residual magnetism of transformer cores under actual working conditions. Environmental and aging factors are often overlooked but are crucial. Long-term thermal cycling can degrade the insulation system, accelerate partial discharge, alter the crystal structure of silicon steel sheets, and ultimately affect the hysteresis characteristics. To sum up, the precise measurement of core remanence in power transformers is crucial for ensuring the safe and stable operation of the power grid.