Pre-Insertion Resistor Temperature Prediction Based on a Novel Loss Function Combining Deep Learning and the Finite Element Method

During transmission line faults, the pre-insertion resistors in circuit breakers accumulate heat and lead to thermal explosion during repeated closing. The risk of thermal explosion can be reduced if the pre-insertion resistor temperature can be accurately predicted. This study proposes a method for predicting the pre-insertion resistor temperature to optimize the cooling time. The overfitting problem is more serious for models using traditional loss functions. To solve this problem, deep learning models based on a new loss function, the rational smoothing loss, are used to predict the temperature of pre-insertion resistors. The rational smoothing loss, inspired by the kernel function, dynamically adjusts the error versus gradient and incorporates constraints for regularization. The coati optimization algorithm with Ornstein–Uhlenbeck mutation optimizes the rational smoothing loss parameters. The results demonstrate that models using rational smoothing loss significantly outperform those with traditional loss functions, showing reductions of 77.97% in mean absolute error and 93.72% in mean square error, reducing the mean absolute error to 0.29 K. Additionally, the prediction curves exhibit remarkable smoothness, indicating the rational smoothing loss’s robustness against overfitting. The accurate prediction of pre-insertion resistor temperature is crucial for safely operating circuit breakers and technically supporting cooling time optimization.