变压器绕组松动故障给电力系统安全稳定埋下巨大隐患，目前缺乏切实有效的诊断方法。因此提出一种基于50 Hz倍频小波时频熵和RUSBoost的变压器绕组松动声纹识别方法。首先，针对变压器声纹特点提出50 Hz倍频小波时频熵，用于声纹信号特征提取。然后，针对变压器故障样本较少导致的样本不平衡的问题，提出基于RUSBoost模型的模式识别。最后，在现场实测数据的基础上验证了该方法的有效性。研究结果表明，所提方法对变压器绕组不同松动程度的故障均能实现可靠诊断，平均识别准确率达到了98.9%。样本较少的75%松动和100%松动的识别准确率也分别高达97.2%和94.6%。相较于RF、DT、KNN以及SVM等传统模型，总体识别准确率至少提高3.3%，样本较少的75%松动和100%松动的识别准确率至少提高了2.8%和2.5%。
The looseness fault of transformer winding leaves a huge hidden danger for the safety and stability of power system, but there is a lack of practical and effective diagnosis methods at present. Therefore, a method of transformer winding looseness voiceprint recognition based on 50 Hz frequency multiplying wavelet time-frequency entropy and RUSBoost is proposed. Firstly, according to the characteristics of transformer voiceprint, the 50 Hz frequency multiplying wavelet time-frequency entropy is proposed to extract the characteristics of voiceprint signal. Then, aiming at the problem of sample imbalance caused by less transformer fault samples, a pattern recognition method based on RUSBoost model is proposed. Finally, the effectiveness of this method is verified on the basis of field measurement data. The research results show that the proposed method can realize reliable diagnosis of transformer winding faults with different degrees of looseness, and the average recognition rate reaches 98.9%. The recognition accuracies of 75% looseness and 100% looseness with fewer samples are as high as 97.2% and 94.6%, respectively. The total recognition accuracy is improved by at least 3.3% compared with traditional models such as RF, DT, KNN and SVM, and the recognition accuracies of 75% looseness and 100% looseness with fewer samples are improved by at least 2.8% and 2.5%, respectively.