Abstract: To solve the problem of identifying series arc faults with feature aliasing in low-voltage power scenarios, this paper proposes a time-variance-accuracy coefficient random forest(TVA-Rf) model for low-voltage series arc fault detection. First, analysis of sample feature sensitivity reveals that the core issue stems from reduced sensitivity under specific conditions, leading to inaccuracies in traditional arc detection methods. Secondly, the random forest model is used as the core recognition model due to its anti-interference properties. finally, a TVA coeffit is constructed to achieve multi-objective optimization of random forest hyperparameters, and the random forest model is trained using low-voltage load samples. Through experimental testing of low voltage series voltage, the fault identification method achieves an accuracy rate of 99.97%, and surpasses traditional methods in both accuracy and calculation speed. This proves that the method overcomes feature overlap interference and achieves accurate identification of low voltage series arc faults.

Key words: hyperparameter optimization, random forest, feature sensitivity of samples, series arc fault