林浩凡,张世杰,赵建永,王博闻,金凌峰,杨智,赵涛
LIN Haofan,ZHANG Shijie,ZHAO Jianyong,WANG Bowen,JIN Lingfeng,YANG Zhi,ZHAO Tao

• 1:国网浙江省电力有限公司电力科学研究院
• 2:河北省输变电设备安全防御重点实验室(华北电力大学)
• 3:国网浙江省电力有限公司

摘要(Abstract)：

低频变压器的声纹信号能够反映其工况与异常状态，因此有必要分析其声纹特性。选取一台20 Hz低频变压器，对其在空载、短路和温升试验过程中的声纹信号进行采集，并基于时域和频域分析提取了均方根值、高低频比等特征值。分析结果显示，铁心声纹以40 Hz倍频的谐波分量为主，而绕组声纹主要集中在基频40 Hz上；铁心与绕组振动产生的声音强度随电压或电流的增加而增大，且油温升高会导致变压器振动加剧，噪声增大。此外，低频变压器相较于工频变压器，噪声水平更低，声纹频率复杂度更高。研究结果表明，低频变压器的声纹特性可有效反映其内部状态，为低频变压器的工况识别与故障诊断提供实践依据。
The acoustic signal of a low-frequency transformer can provide insights into its working condition and abnormal states. Therefore, it is essential to analyze its acoustic fingerprint characteristics. A 20 Hz low-frequency transformer was chosen, and its acoustic fingerprint signals were collected during no-load, short-circuit, and temperature-rise tests. Through time-domain and frequency-domain analyses, characteristic values such as the root mean square(RMS) value and the high-low frequency ratio were derived. The analysis results show that the core's acoustic fingerprint signal mainly consists of harmonic components at multiples of 40 Hz, while the winding's acoustic fingerprint signal is predominantly at the fundamental frequency of 40 Hz. The sound intensity resulting from the vibration of the core and winding intensifies with the increase in voltage or current. Moreover, an elevation in oil temperature exacerbates transformer vibration and amplifies noise. Additionally, compared to power-frequency transformers, low-frequency transformers exhibit lower noise levels and greater complexity in acoustic fingerprint frequencies. These research findings demonstrate that the acoustic fingerprint characteristics of low-frequency transformers can effectively mirror their internal states, providing a practical foundation for operating condition identification and fault diagnosis.

关键词(KeyWords)： 低频输电;变压器;声纹特性;均方根值;铁心振动;绕组振动;变压器温升
low-frequency power transmission;transformer;acoustic fingerprint characteristics;RMS value;core vibration;winding vibration;transformer temperature rise

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211DS23000B）

作者(Author): 林浩凡,张世杰,赵建永,王博闻,金凌峰,杨智,赵涛
LIN Haofan,ZHANG Shijie,ZHAO Jianyong,WANG Bowen,JIN Lingfeng,YANG Zhi,ZHAO Tao

DOI: 10.19585/j.zjdl.202506010

参考文献(References)：

• [1] FUNAKI T,MATSUURA K.Feasibility of the low frequency AC transmission[C]//Power Engineering Society Winter Meeting.Singapore:IEEE,2000:2693-2698.
• [2]林进钿，倪晓军，裘鹏.柔性低频交流输电技术研究综述[J].浙江电力，2021,40(10):42-50.LIN Jintian,NI Xiaojun,QIU Peng. Review of flexible low-frequency AC transmission technology[J]. Zhejiang Electric Power,2021,40(10):42-50.
• [3]张文杰，吕世轩，高启瑄，等.基于固态变压器的交直流混合配电系统协调运行控制策略[J].电力建设，2023,44(4):103-112.ZHANG Wenjie,LüShixuan,GAO Qixuan,et al.Coordinated operation control strategy of AC/DC hybrid distribution system based on solid-state transformer[J]. Electric Power Construction,2023,44(4):103-112.
• [4]徐斌，甄超，王小明，等.大功率冲击性负荷低频间谐波检测方法[J].电力电容器与无功补偿，2023,44(3):65-71.XU Bin,ZHEN Chao,WANG Xiaoming,et al.Detection method for low freuency interharmonics under high power impact load[J].Power Capacitor&Reactive Power Compensation,2023,44(3):65-71.
• [5]彭发喜，陈建福，袁智勇，等.海上风电柔性低频输电动模平台设计[J].电力电容器与无功补偿，2024,45(5):108-116.PENG Faxi,CHEN Jianfu,YUAN Zhiyong,et al.Design of dynamic model platform for offshore wind farm interconnection via flexible low-frequency AC transmission system[J]. Power Capacitor&Reactive Power Compensation,2024,45(5):108-116.
• [6]相晨萌，高树国，刘宏亮，等.基于宽频振动传感器的电力设备状态监测方法可行性分析[J].高压电器，2024,60(4):83-91.XIANG Chenmeng,GAO Shuguo,LIU Hongliang,et al.Feasibility analysis of power equipment condition monitoring method based on broadband vibration sensor[J].High Voltage Apparatus,2024,60(4):83-91.
• [7]马玉龙，宋胜利，王玲，等.提高特高压直流运行可靠性的分接开关优化控制策略[J].电力建设，2024,45(4):37-45.MA Yulong,SONG Shengli,WANG Ling,et al. Optimized control strategy for less action frequency of tap changers in UHVDC[J]. Electric Power Construction,2024,45(4):37-45.
• [8]李琪菡，张振东，刘浩，等.影响油浸式电力变压器振动因素的研究[J].电测与仪表，2023,60(9):96-101.LI Qihan,ZHANG Zhendong,LIU Hao,et al. Study on factors affecting vibration of oil immersed power transformers[J]. Electrical Measurement&Instrumentation,2023,60(9):96-101.
• [9]顾仲翔，马宏忠，张勇，等.基于VMD与优化SVM的变压器绕组松动缺陷振动信号诊断方法[J].高压电器，2023,59(1):117-125.GU Zhongxiang,MA Hongzhong,ZHANG Yong,et al.Vibration signal diagnosis method of transformer winding looseness defect based on VMD and optimized SVM[J].High Voltage Apparatus,2023,59(1):117-125.
• [10]王博闻.电力变压器典型异常状态声纹诊断技术[D].北京：华北电力大学，2022.WANG Bowen. Acoustic signal diagnosis technology for typical anomalous state of power transformer[D].Beijing:North China Electric Power University,2022.
• [11]高盛楠，潘超，安景革，等.基于特征提取映射的变压器偏磁多场响应[J].电网与清洁能源，2023,51(8):45-54.GAO Shengnan,PAN Chao,AN Jingge,et al.Multi-field response of transformer bias based on feature extraction mapping[J]. Power System and Clean Energy,2023,51(8):45-54.
• [12]姜言金，刘栩霏，徐晓东，等.基于多场耦合的变压器直流偏磁噪声异常表征[J].电网与清洁能源，2023,51(1):11-19.JIANG Yanjin,LIU Xufei,XU Xiaodong,et al.Anomaly characterization of transformer DC bias noise based on multi-field coupling[J].Power System and Clean Energy,2023,51(1):11-19.
• [13]刘云鹏，王博闻，周旭东，等.基于等效源法的变压器瞬态声场重构与声学监测测点位置定量评价方法[J].中国电机工程学报，2022,42(7):2765-2776.LIU Yunpeng,WANG Bowen,ZHOU Xudong,et al.Method of transformer transient acoustic field reconstruction based on equivalent source method and acoustic monitoring point position quantitative evaluation[J]. Proceedings of the CSEE,2022,42(7):2765-2776.
• [14] LV X B,XU M K,WANG S Y,et al. Development of double channel transformer voiceprint detection system based on MEMS digital silicon microphone[J].Journal of Physics:Conference Series,2023,2495(1):012030.
• [15]刘云鹏，王博闻，李欢，等.结合载纤绕组形变测量法的大型变压器绕组多次短路冲击暂态声纹特征[J].中国电机工程学报，2022,42(1):434-447.LIU Yunpeng,WANG Bowen,LI Huan,et al.Transient acoustics characteristics of large transformer windings under multiple short-circuit impulse combined with fibercarrying winding deformation measurement[J]. Proceedings of the CSEE,2022,42(1):434-447.
• [16]刘云鹏，王博闻，周旭东，等.基于162台超、特高压变压器的声纹特征预警阈值划定研究[J].华北电力大学学报（自然科学版），2021,48(5):45-53.LIU Yunpeng,WANG Bowen,ZHOU Xudong,et al.Threshold delineation research for early warning of voiceprint eigenvalues based on 162 sets of EHV and UHV transformers[J]. Journal of North China Electric Power University(Natural Science Edition),2021,48(5):45-53.
• [17]于浩.谐波激励下电力变压器振动特性分析[J].电气技术与经济，2024(3):152-154.YU Hao. Analysis of vibration characteristics of power transformer under harmonic excitation[J].Electrical Equipment and Economy,2024(3):152-154.
• [18] HU J Z,ZHOU B,WANG Y Z,et al.Analysis of vibration characteristics and noise reduction for 10 kV oilimmersed transformer[J].Fluctuation and Noise Letters,2023,22(6):2350048.
• [19] WANG H,ZHANG X M,ZHANG X L,et al.Voiceprint feature extraction of large power transformers in uninterrupted diagnosis and early warning technology[J].Journal of Physics:Conference Series,2022,2310(1):012050.
• [20]滕千里，李卫军，马思聪，等.660 MW汽轮机组低压缸异常振动的分析及治理[J].浙江电力，2023,42(7):110-116.TENG Qianli,LI Weijun,MA Sicong,et al.Analysis and treatment of abnormal vibration for low-pressure cylinder of 660 MW steam turbine units[J]. Zhejiang Electric Power,2023,42(7):110-116.
• [21]余长树.基于声纹的变压器故障诊断算法及其应用研究[D].北京：华北电力大学，2023.YU Changshu.sound pattern based transformer fault diagnosis algorithm and application research[D]. Beijing:North China Electric Power University,2023.
• [22]张重远，罗世豪，岳浩天，等.基于Mel时频谱-卷积神经网络的变压器铁芯声纹模式识别方法[J].高电压技术，2020,46(2):413-423.ZHANG Zhongyuan,LUO Shihao,YUE Haotian,et al.Pattern recognition of acoustic signals of transformer core based on mel-spectrum and CNN[J].High Voltage Engineering,2020,46(2):413-423.
• [23] SUN Y T,MA H Z.Research progress on oil-immersed transformer mechanical condition identification based on vibration signals[J]. Renewable and Sustainable Energy Reviews,2024,196:114327.
• [24]曹力潭，魏华兵，黄智，等.基于LSTM神经网络的电抗器故障声纹识别方法[J].浙江电力，2023,42(4):114-120.CAO Litan,WEI Huabing,HUANG Zhi,et al.Research on voiceprint recognition of reactor fault based on LSTM neural network[J].Zhejiang Electric Power,2023,42(4):114-120.
• [25]高沛，王丰华，苏磊，等.直流偏磁下电力变压器的振动特性[J].电网技术，2014,38(6):1536-1541.GAO Pei,WANG Fenghua,SU Lei,et al.Analysis on vibration characteristics of power transformer under DC bias[J].Power System Technology,2014,38(6):1536-1541.
• [26]师愉航，汲胜昌，张凡，等.变压器绕组多倍频振动机理及特性[J].高电压技术，2021,47(7):2536-2544.SHI Yuhang,JI Shengchang,ZHANG Fan,et al. Multifrequency vibration mechanism and characteristics of transformer windings[J]. High Voltage Engineering,2021,47(7):2536-2544.
• [27]吴晓文，周年光，彭继文，等.电力变压器噪声特性与相关因素分析[J].电力科学与技术学报，2018,33(3):81-85.WU Xiaowen,ZHOU Nianguang,PENG Jiwen,et al.Noise characteristic and relevant factors analysis of power transformers[J]. Journal of Electric Power Science and Technology,2018,33(3):81-85.
• [28]洪剑锋，曹君慈，刘亚静.永磁电机电磁振动及变开关频率振动抑制技术试验探究[J].电机与控制学报，2023,27(12):52-61.HONG Jianfeng,CAO Junci,LIU Yajing. Experimental research on PWM technique for vibration reduction in PM motor[J].Electric Machines and Control,2023,27(12):52-61.
• [29]李勇，许洪华，何萍，等.220 kV自耦变压器声纹测试分析[J].电气自动化，2023,45(2):18-21.LI Yong,XU Honghua,HE Ping,et al.Test and analysis of 220 kV autotransformer’s voiceprint[J].Electrical Automation,2023,45(2):18-21.
• [30]刘锟.不同结构形式的铁心构件电磁与声振特性研究[D].沈阳：沈阳工业大学，2023.LIU Kun.Study on electromagnetic and acoustic vibration characteristics of Iron core components with different structural forms[D]. Shenyang:Shenyang University of Technology,2023.
• [31]何文林，郑一鸣.基于振动检测的变压器绕组机械稳定性评估[J].浙江电力，2022,41(7):57-62.HE Wenlin,ZHENG Yiming. Evaluation on mechanical stability of transformer windings based on vibration detection[J].Zhejiang Electric Power,2022,41(7):57-62.
• [32]莫娟，张霞，袁建生.变压器空载与短路噪声频谱特性分析[J].电工电能新技术，2015,34(2):76-80.MO Juan,ZHANG Xia,YUAN Jiansheng. Analysis of transformer noise frequency spectrum under short-circuit and no-load conditions[J].Advanced Technology of Electrical Engineering and Energy,2015,34(2):76-80.
• [33]国家质量监督检验检疫总局，中国国家标准化管理委员会.电力变压器第2部分：液浸式变压器的温升：GB/T 1094.2—2013[S].北京：中国标准出版社，2014.
• [34]李付贵.油浸式变压器振动特性分析及油温变化对振动的影响研究[D].南京：东南大学，2017.LI Fugui.Finite element analysis on the vibration characteristics of oil-immersed transformer and the influence of transformer oil to vibration.Nanjing:Southeast University,2017.