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KHA-CNN,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。
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 time-frequency 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 S-time-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.
作者
柴斌
韦鹏
宁复茂
姚琪
李辉
CHAI Bin;WEI Peng;NING Fumao;YAO Qi;LI Hui(State Grid Ningxia Ultrahigh Voltage Company,Yinchuan 750011,Ningxia,China;State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan 750001,Ningxia,China;School of Electrical Engineering,Xi'an University of Technology,Xi’an 710054,Shaanxi,China)
出处
《电网与清洁能源》
CSCD
北大核心
2024年第2期103-109,共7页
Power System and Clean Energy
基金
国家自然科学基金项目(51779206)
宁夏自然科学基金项目(2022AAC03631)。
关键词
换流变压器
声纹信号
S变换时频谱图
磷虾群优化算法
卷积神经网络
converter transformer
voiceprint signal
S transform time-frequency spectrum
krill herd algorithm
convolutional neural network
