为提高基于机器学习的变压器故障诊断精度，提出了基于油中溶解气体分析（Dissolved Gas Analysis，DGA）的NRBO-XGBoost变压器故障诊断方法。选择极度梯度提升决策树（Extreme Gradient Boosting，XGBoost）模型，结合牛顿-拉夫逊算法（Newton-Raphson-based optimizer，NRBO），通过迭代过程对模型进行寻找最优参数设置，每一轮迭代都会评估当前解决方案的性能，使用找到的最优参数重新训练XGBoost模型，根据比较优化前后的结果，可以明显看到模型性能的提升。通过算例分析对建立的NRBO-XGBoost方法性能进行评估，验证了所提方法对变压器故障诊断的有效性，且收敛性较好，精度较高。

To enhance the accuracy of transformer fault diagnosis based on machine learning, a transformer fault diagnosis method using NRBO-XGBoost based on Dissolved Gas Analysis (DGA) is proposed. The Extreme Gradient Boosting (XGBoost) model is selected. The Newton-Raphson-based optimizer (NRBO) is combined with XGBoost to iteratively search for the optimal model parameters. In each iteration, the performance of the current solution is evaluated, and the XGBoost model is retrained using the optimal parameters obtained. A significant improvement in model performance is observed by comparing the results before and after optimization. The performance of the NRBO-XGBoost method is evaluated through a case study, demonstrating the effectiveness of the proposed method for transformer fault diagnosis, with good convergence and high accuracy.