Improved Crow Search Algorithm and XGBoost for Transformer Fault Diagnosis

Abstract

To enhance the accuracy of transformer fault diagnosis, this study proposes an enhanced transformer fault diagnosis model incorporating the Improved Crow Search Algorithm (ICSA) and XGBoost. The dissolved gas analysis in oil (DGA) technique is employed to extract 9-dimensional fault features of transformers as model inputs, in conjunction with the codeless ratio method for training. The output layer utilizes a gradient boosting-based decision tree addition model to obtain the fault diagnosis type. Furthermore, the Golden Sine Algorithm (GSA) is employed for improvement, and the ICSA’s performance is tested by using typical test functions, demonstrating faster convergence and stronger merit-seeking capabilities. The obtained results reveal that the comprehensive diagnostic accuracy of the proposed model reaches 94.4056%, marking an improvement of 8.3916%, 6.2937%, 4.1958%, and 2.0979% compared to the original base XGBoost, PSO-XGBoost, GWO-XGBoost, and CSA-XGBoost fault diagnosis models, respectively. These findings validate the effectiveness of the proposed method in enhancing the fault diagnosis performance of transformers.