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作 者:孙安青 贾廷波 王丰华[2] 杨秀龙 岳美 许景华 SUN Anqing;JIA Tingbo;WANG Fenghua;YANG Xiulong;YUE Mei;XU Jinghua(Rizhao Power Supply Company of State Grid Shandong Electric Power Company,Rizhao,Shandong 276800,China;Key laboratory of Control of Power Transmission and Conversion,Shanghai Jiaotong University,Shanghai 200240,China)
机构地区:[1]国网山东省电力公司日照供电公司,山东日照276800 [2]电力传输与功率变换教育部重点实验室(上海交通大学),上海200240
出 处:《广东电力》2022年第9期127-134,共8页Guangdong Electric Power
基 金:国网山东省电力公司科技项目(520617210002)。
摘 要:运行中的变压器声信号以其非接触式测量、高灵敏度和强时效性等优点逐渐成为基于数据驱动模型的变压器状态监测领域的关注热点,如何从非平稳的声信号中获取合理有效及区分度高的声纹特征参数是变压器状态监测的关键。对此,在分别计算变压器声信号Mel频率倒谱系数和常Q倒谱系数特征参数的基础上,引入二维主成分分析法对这2种声纹特征参数进行融合降维,旨在降低数据维数的同时获取主要声纹特征,并以平均类间区分度表征声纹特征参数的区分度。某变压器不同直流偏磁状态下声信号的计算结果表明,所提出的变压器声信号特征参数表征方法更加全面高效且区分度高,可为变压器运行状态声信号监测技术提供重要参考。Acoustic signals for an operating transformer gradually becomes a hot spot in the field of condition monitoring of transformer based on the data-driven model with the advantages of non-contact measurement,high sensitivity and strong lines.Hence,the key problem is to obtain effective characteristic parameters of voiceprint with high discrimination from those non-stationary acoustic signals.On the basis of calculating Mel frequency cepstrum coefficient(MFCC)and Gammatone constant Q cepstrum coefficient(CQCC)of the acoustic signals,the two-dimensional principal component analysis(2DPCA)is used for dimension reduction fusion of the two kinds of characteristic parameters,so as to obtain the main voiceprint features and describe the discrimination of characteristic parameters of acoustic signals according to the average inter-class discrimination.The calculated results of characteristic parameters of acoustic signals for a transformer under DC bias indicate that the proposed method for the description of characteristic parameters of acoustic signals of transformer is more comprehensive and effective with high discrimination,which can provide the important reference for the condition monitoring of transformer with acoustic signals.
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