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Abstract：
In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, the Synthetic Minority Over-sampling Technique (SMOTE) was used to expand the minority samples. Secondly, the non-coding ratio method was used to construct multi-dimensional feature parameters, and the Light Gradient Boosting Machine (LightGBM) feature optimization strategy was introduced to screen the optimal feature subset. Finally, Northern Goshawk Optimization (NGO) algorithm was used to optimize the parameters of Gradient Boosting Decision Tree (GBDT), and then the transformer fault diagnosis was realized. The results show that the proposed method can reduce the misjudgment of minority samples. Compared with other integrated models, the proposed method has high fault identification accuracy, low misjudgment rate and stable performance.
摘要：
为了提高变压器故障诊断的准确性,改善模型训练不足导致的不平衡样本对模型辨识精度低的影响,提出了一种基于SMOTE和NGO-GBDT的变压器故障诊断方法。首先,使用合成少数过采样技术（SMOTE）来扩展少数样本。其次,采用非编码比方法构造多维特征参数,引入光梯度提升机(LightGBM)特征优化策略筛选最优特征子集。最后,采用NorthernGoshawk优化(NGO)算法对梯度提升决策树(GBDT)参数进行优化,实现了变压器故障诊断。结果表明,该方法可以减少少数样本的误判。与其他集成模型相比，该方法具有较高的故障识别精度,误判率低，性能稳定。
