Power Transformer Modelling for the Evaluation of a Reinforcement Learning-Based Inrush Current Minimization Strategy

Abstract

Inrush currents in power transformers can affect the transformer lifetime and the reliability of electrical grids. Various inrush current minimization techniques have been proposed to mitigate these effects. To validate any minimization strategy, an accurate transformer model is required. This study focuses on developing a transformer model for inrush current minimization. The transformer is modelled through duality-based transformation, recognized for its accurate core representation. In addition, the laboratory environment and its constituent components are also modelled. The developed model is validated against real inrush current measurements. Finally, the model is employed to test a Reinforcement Learning (RL) based inrush current minimization strategy. The performance of the proposed method is validated by contrasting its outcomes with inrush current data obtained from traditional uncontrolled Circuit Breaker (CB) operations. The findings indicate that the proposed inrush current minimization approach reduces the peak inrush current by 77% compared to traditional uncontrolled CB switching.