【摘要】 为准确进行换流变压器故障诊断，保证直流输电可靠性，提出一种基于S变换时频谱和KHA-CNN模式的换流变故障声纹识别方法。利用自适应补充集合经验模态分解算法（complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN），实现换流变本体声纹信号与噪声的分离；通过S变换得到时频谱图，实现声纹信号的特征提取；通过磷虾群优化算法（krill herd algorithm,KHA）对卷积神经网络进行超参数寻优，将S时频谱图作为特征输入到KHACNN，实现故障诊断。研究结果表明：该方法对于换流变故障具有很好的识别效果，能为换流变故障诊断提供有效参考。更多还原

【Abstract】 Aiming to accurately diagnose converter transformer faults and ensure the reliability of DC transmission,this paper proposes a method for the voiceprint recognition of converter transformer faults based on S Transform timefrequency spectrum and KHA-CNN patterns. Firstly, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)algorithm is used to separate the voiceprint signal and noise of the converter body;secondly,the time-frequency spectrum is obtained by the S-transform to achieve extraction of features of the voiceprint signal. Finally,the Krill Herd Algorithm(KHA) is used to optimize the convolutional neural network with hyperparameters,and the Stime-frequency spectrogram is used as the feature input to the KHA-CNN for fault diagnosis. The research results show that the method has good recognition effect for converter faults and provides effective reference for converter fault diagnosis.更多还原