受数据样本难以区分和数据平衡性不佳影响，采用声振信号的变压器状态识别模型往往准确率低下。针对这一问题，引入了Focal损失，根据样本训练过程的准确度动态反馈权重，从而构成了Focal-XGBoost优化模型。先通过一组贴合变压器频谱的滤波器充分提取声振信号有效信息，再作XGBoost-PCA筛选降低样本维度。然后采用Focal损失优化原模型中的Softmax目标函数形成Focal-XGBoost模型，并在输入上述样本后根据准确率波动作Focal的超参数优化，进而输出变压器状态识别结果。10 kV和110 kV变压器的试验结果表明，相较传统SVM、KNN等学习模型，Focal-XGBoost减少了XGBoost测试样本中难分样本的误分量44.7%，从而使模型识别准确率更高；此外，非均匀提取在平均精度损失低于0.5%的基础上压缩50%样本空间，进一步降低了模型训练成本。

Influenced by indistinguishable data samples and poor data balance, transformer state identification models using vibro-acoustic signals often have low accuracy. To address this problem, Focal loss is introduced to dynamic feedback weights according to the accuracy of the sample training process, thus constituting a Focal-XGBoost optimization model. Firstly, a set of filters that fit the transformer spectrum are used to fully extract the effective information of the vibro-acoustic signal, and then XGBoost-PCA is used to reduce the dimensionality of the samples. Then, the Softmax objective function in the original model is optimized using Focal loss to form the Focal-XGBoost model. After inputting the above samples, the hyperparameters of Focal are optimized based on the accuracy wave action, and the transformer state recognition results are output. The experimental results of 10 kV and 110 kV transformers show that Focal-XGBoost can reduce the number of samples compared with traditional SVM and KNN models. Focal-XGBoost reduces the misspecification of difficult samples in XGBoost test samples by 44.7%, which results in higher model recognition accuracy. In addition, non-uniform extraction compresses the sample space by 50% on the basis of average accuracy loss below 0.5%, which further reduces the model training cost.

江苏省电力有限公司重点科技项目(J2021053)