曹力潭,魏华兵,黄智,石明垒
CAO Litan,WEI Huabing,HUANG Zhi,SHI Minglei

• 1:国网浙江省电力有限公司超高压分公司

摘要(Abstract)：

高压电抗器是保障电力系统安全稳定运行的重要设备之一，针对高压电抗器故障难以被准确识别的问题，提出了基于LSTM（长短期记忆）神经网络的高压电抗器故障声纹识别方法。首先对一台高压电抗器运行时产生的声纹信号进行收集，然后将声纹信号分为若干个声纹片段后转换为语谱图，并使用Mel时频谱降维处理，最后采用LSTM网络对语谱图进行高压电抗器故障识别。实验结果表明，所提出的方法实现了高压电抗器故障的智能化诊断，有效提高故障识别的精准度，减少故障检测时所需投入的人力，助推电网安全监测智能化水平的提升。
High-voltage reactor is one of the critical equipment to ensure the safe and stable operation of power system. As the fault of high-voltage reactor is difficult to be identified accurately, a voiceprint recognition method based on LSTM(long short-term memory) neural network for high-voltage reactor faults is proposed. Firstly, the voiceprint signals generated during the operation of a high-voltage reactor are collected. Then the signals are divided into several segments, converted into a spectrogram, and the Mel time spectrum is used to reduce the dimensionality. Finally, the LSTM network is used to identify the high-voltage reactor faults in the spectrogram. The experimental results show that the proposed method can realize the intelligent diagnosis of high-voltage reactor faults, effectively improve the accuracy of fault identification, reduce the manpower required for fault detection, and improve the intelligent level of power grid safety monitoring.

关键词(KeyWords)： 高压电抗器;长短期记忆网络;声纹识别;故障;语谱图
high-voltage reactor;LSTM network;voiceprint recognition;fault;spectrogram

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211MR20004U）

作者(Author): 曹力潭,魏华兵,黄智,石明垒
CAO Litan,WEI Huabing,HUANG Zhi,SHI Minglei

DOI: 10.19585/j.zjdl.202304014

参考文献(References)：

• [1]余长厅，黎大健，陈梁远，等.基于声纹及振动的变压器故障诊断技术研究[J].高压电器，2019,55(11):248-254.YU Zhangting,LI Dajian,CHEN Liangyuan,et al.Transformer fault diagnosis technique based on voiceprint and vibration[J].High Voltage Apparatus,2019,55(11):248-254.
• [2]刘云鹏，王博闻，岳浩天，等.基于50 Hz倍频倒谱系数与门控循环单元的变压器偏磁声纹识别[J].中国电机工程学报，2020,40(14):4681-4694.LIU Yunpeng,WANG Bowen,YUE Haotian,et al.Identification of transformer bias voiceprint based on 50 Hz frequency multiplication cepstrum coefficients and gated recurrent unit[J].Proceedings of the CSEE,2020,40(14):4681-4694.
• [3]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.
• [4]陈青恒，马宏彬，何金良.直流偏磁引起的500 kV电力变压器振动和噪声的现场测量与分析[J].高压电器，2009,45(3):93-96.CHEN Qingheng,MA Hongbin,HE Jinliang.Field monitoring and analysis on vibration and noise of 500 kV electrical transformer under DC current biasing[J].High Voltage Apparatus,2009,45(3):93-96.
• [5]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.
• [6]吴晓文，周年光，裴春明，等.特高压交流变电站可听噪声分离方法[J].高电压技术，2016,42(8):2625-2632.WU Xiaowen,ZHOU Nianguang,PEI Chunming,et al.Separation methodology of audible noises of UHV AC substations[J].High Voltage Engineering,2016,42(8):2625-2632.
• [7]王丰华，王邵菁，陈颂，等.基于改进MFCC和VQ的变压器声纹识别模型[J].中国电机工程学报，2017,37(5):1535-1543.WANG Fenghua,WANG Shaojing,CHEN Song,et al.Voiceprint recognition model of power transformers based on improved MFCC and VQ[J]. Proceedings of the CSEE,2017,37(5):1535-1543.
• [8]陈继瑞，李宝伟，倪传坤，等.基于电感特征的并联电抗器匝间短路故障识别方法[J].电力系统自动化，2022,46(20):167-173.CHEN Jirui,LI Baowei,NI Chuankun,et al. Inter-turn short-circuit fault identification method for shunt reactor based on inductance characteristics[J]. Automation of Electric Power Systems,2022,46(20):167-173.
• [9]潘信诚，马宏忠，徐艳，等.基于MPE和灰色关联度的高压并联电抗器故障诊断方法[J].大电机技术，2020(4):70-74.PAN Xincheng,MA Hongzhong,XU Yan,et al.Fault diagnosis method of high voltage shunt reactor based on MPE and gray correlation degree[J].Large Electric Machine and Hydraulic Turbine,2020(4):70-74.
• [10]陈鹏安.基于GAN的高压电抗器故障振动信号生成技术研究[D].武汉：华中科技大学，2020.CHEN Peng'an.Research on GAN-based high-voltage reactor fault vibration signal generation technology[D].Wuhan:Huazhong University of Science and Technology,2020.
• [11]胡爱军，连俭，向玲.基于ACNN和Bi-LSTM的风电机组故障早期识别[J].太阳能学报，2021,42(12):143-149.HU Aijun,LIAN Jian,XIANG Ling.Early fault identification of wind turbine based on acnn and bi-lstm[J].Acta Energiae Solaris Sinica,2021,42(12):143-149.
• [12]唐赛，何荇兮，张家悦，等.基于长短期记忆网络的轴承故障识别[J].汽车工程学报，2018,8(4):297-303.TANG Sai,HE Xingxi,ZHANG Jiayue,et al. Bearing fault identification based on long short-term memory networks[J]. Chinese Journal of Automotive Engineering,2018,8(4):297-303.
• [13]郭利爽，马宏涛，李凤婷.基于电抗器电压暂态特性的直流配电网故障检测方案[J].电力电容器与无功补偿，2021,42(2):72-78.GUO Lishuang,MA Hongtao,LI Fengting. Fault detection scheme for DC distribution network based on voltage transient characteristics of reactor[J].Power Capacitor&Reactive Power Compensation,2021,42(2):72-78.
• [14]张建敏.基于振动信号的并联电抗器故障检测方法研究[D].北京：华北电力大学，2020.ZHANG Jianmin. Research on fault detection method of shunt reactor based on vibration signal[D].Beijing:North China Electric Power University,2020.
• [15]滕予非，吴杰，张真源，等.基于离群点检测的高压并联电抗器本体电流互感器测量异常故障在线诊断[J].电工技术学报，2019,34(11):2405-2414.TENG Yufei,WU Jie,ZHANG Zhenyuan,et al. Online identification of measurement abnormality fault based on outlier detection for current transformer in high voltage shunt reactor[J]. Transactions of China Electrotechnical Society,2019,34(11):2405-2414.