Transformer fault diagnosis using machine learning: a method combining SHAP feature selection and intelligent optimization of LGBM

Abstract

This paper proposes a novel approach for transformer fault diagnosis. Initially, a high-dimensional feature set comprising 19 features related to five gas concentrations is constructed to reflect the gas-fault relationship. Subsequently, the Shapley Additive Explanations (SHAP) method is employed to evaluate feature importance and select a subset that significantly influences model predictions, thereby simplifying the model and enhancing its interpretability. Following this, the bald eagle search (BES) intelligent optimization algorithm is utilized to optimize the hyperparameters of the light gradient boosting machine (LGBM) model, further improving its predictive capability. Comparative experiments with various traditional machine learning models validate the effectiveness of the proposed method. The SHAP-BES-LGBM model achieves the highest accuracy of 0.9509 and an f1 score of 0.9606 on the test set, with only 11 samples misclassified, demonstrating superior classification performance and underscoring the advantages of this integrated approach in transformer fault diagnosis.