Vibration Signal Feature Prediction of GIS Equipment Based on Temporal Convolution Network

WANG Qian; JIANG Xiping; LONG Yingkai; ZHANG Shiling; HU Dong; ZHAO Zhongyong; YANG Tongliang

doi:10.13296/j.1001-1609.hva.2026.02.002

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Vibration Signal Feature Prediction of GIS Equipment Based on Temporal Convolution Network

Research & Analysis | 更新时间：2026-03-16

- Vibration Signal Feature Prediction of GIS Equipment Based on Temporal Convolution Network
- High Voltage Apparatus Vol. 62, Issue 2, Pages: 8-18(2026)
- 作者机构：
  
  国网重庆电力公司电力科学研究院，重庆 401123
  西南大学工程技术学院，重庆 400715
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.02.002
  CLC：
- Received：21 July 2025，
  
  Revised：2025-10-02，
  
  Published：16 February 2026
- 稿件说明：
移动端阅览
WANG Qian, JIANG Xiping, LONG Yingkai, et al. Vibration Signal Feature Prediction of GIS Equipment Based on Temporal Convolution Network[J]. High Voltage Apparatus, 2026, 62(2): 8-18.
DOI：

WANG Qian, JIANG Xiping, LONG Yingkai, et al. Vibration Signal Feature Prediction of GIS Equipment Based on Temporal Convolution Network[J]. High Voltage Apparatus, 2026, 62(2): 8-18. DOI： 10.13296/j.1001-1609.hva.2026.02.002.

摘要

GIS设备振动信号的变化可以反映设备内部的机械状态。为了提高GIS设备振动信号特性的预测精度，文中提出了一种基于分解—预测—重构的组合组测模型。首先，基于GIS历史振动信号，通过傅里叶变换在频域提取振动特征参数；其次，为了尽可能消除振动特征参数序列非平稳特性带来的影响，将归一化后的序列使用经过粒子群算法（PSO）优化后的变分模态分解（VMD）对振动特征参数序列进行分解；最后，将分解得到的一组平稳化模态分量使用时间卷积网络（TCN）进行预测。实验结果表明，文中所提基于PSO-VMD-TCN的组合预测模型预测结果均方根误差和平均绝对百分比误差分别为1.79%与0.13%，在预测精度上优于其他方法，有利于GIS设备前期故障诊断。

Abstract

The variation of vibration signal of GIS equipment can reflect the mechanical condition inside the equipment. For improving the prediction accuracy of vibration signal characteristics of GIS equipment

in this paper a combined group measurement model based on decomposition-forecasting-reconstruction is proposed. First

based on historical vibration signals of GIS

vibration characteristic parameters are extracted in frequency domain by Fourier transform. Then

in order to eliminate as much as possible the influence due to the non-stationary characteristics of the vibration characteristic parameter sequence

the normalized sequence is decomposed by the variational mode decomposition (VMD) optimized by particle swarm optimization (PSO). Finally

the time convolution network (TCN) is used to predict a set of stationary modal components obtained by decomposition. The experimental results show that the root mean square error and the average absolute percentage error of the combined prediction model based onPSO-VMD-TCNproposed in this paper are 1.79% and 0.13%

respectively

which are superior to other methods in prediction accuracy and are conducive to the early fault diagnosis of GIS equipment.

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references

张斌斌,刘晓洲.变电站GIS设备SF 6 在线监测系统分析及应用[J ] .技术与市场,2019,26(12):103-104.

ZHANG Binbin, LIU Xiaozhou. Analysis and application of SF 6 on line monitoring system of substation GIS equipment[J ] . Technology and Market, 2019, 26(12):103-104.

孙宇晗,程永锋,卢智成,等. 170 kV中性点电抗器地震模拟振动台试验研究[J].振动与冲击,2018,37(13):229-234.

SUN Yuhan, CHENG Yongfeng, LU Zhicheng, et al.Earthquake simulation shaking table tests for a 170 kV neutral reactor[J]. Journal of Vibration and Shock, 2018, 37(13):229-234.

周兵,王延召,胡静竹,等.并联电抗器振动特性及声功率级计算[J].高电压技术,2019,45(11):3685-3692.

ZHOU Bing, WANG Yanzhao, HU Jingzhu, et al.Vibration characteristics and acoustic power level calculation of shunt reactors[J]. High Voltage Engineering, 2019, 45(11):3685-3692.

王磊磊,张嵩阳,姚德贵,等.变电站铁心电抗器振动与可听噪声研究综述[J].高压电器,2019,55(11):26-33.

WANG Leilei, ZHANG Songyang, YAO Degui, et al.Vibration and audible noise of iron-core reactor in substation:A review[J]. High Voltage Apparatus, 2019, 55(11):26-33.

侯鹏飞,马宏忠,吴金利,等.基于混沌理论与蝗虫优化K-means聚类算法的电抗器铁芯和绕组松动状态监测[J].电力自动化设备,2020,40(11):181-187.

HOU Pengfei, MA Hongzhong, WU Jinli, et al. Looseness status monitoring of reactor core and winding based on chaos theory and K-means clustering algorithm optimized by grasshopper algorithm[J]. Electric Power Automation Equipment, 2020, 40(11):181-187.

高树国,汲胜昌,孟令明,等.基于在线监测系统与声振特征预测模型的高压并联电抗器运行状态评估方法[J].电工技术学报,2022,37(9):2179-2189.

GAO Shuguo, JI Shengchang, MENG Lingming, et al.Operation state evaluation method of high-voltage shunt reactor based on on-line monitoring system and vibro-acoustic characteristic prediction model[J]. Transactions of China Electrotechnical Society, 2022, 37(9):2179-2189.

DAS S,SIDHU T S,ZADEH M R D,et al. A novel hybrid differential algorithm for turn to turn fault detection in shunt reactors[J]. IEEE Transactions on Power Delivery,2017,32(6):2537-2545.

朱明,黄勤清,齐用卡,等.基于振动信号总离散频谱的高压并联电抗器健康评估方法[J].电测与仪表,2023,60(8):114-120.

ZHU Ming, HUANG Qinqing, QI Yongka, et al.Health assessment method of high voltage shunt reactor based on total discrete spectrum of vibration signals[J]. Electrical Measurement &Instrumentation, 2023, 60(8):114-120.

ZHAO Yajun,CHEN Xiaohan,CHENG Xiang,et al.Noninvasive method for online detection of internal winding faults of 750 kV EHV shunt reactors[J]. IEEE Transactions on Dielectrics &Electrical Insulation,2015(22):2833-2840.

JIANG Ning,HAO Baoxing,ZHAO Ruoyu,et al.Application of empirical wavelet transform in vibration signal analysis of UHV shunt reactor[C]//IEEE Milan Power Tech.Milan,Italy:IEEE,2019:1-5.

WU Deng,LIU Hailong,XU Junjie,et al.An improved quantuminspired differential evolution algorithm for deep belief network[J]. IEEE Transactions on Instrumentation & Measurement,2020,69 (10):7319-7327.

潘信诚,马宏忠,陈明,等.基于CRP和RQA的高压并联电抗器振动信号分析[J].大电机技术,2019(3):62-67.

PAN Xincheng, MA Hongzhong, CHEN Ming, et al.Analysis of vibration signals of high voltage shunt reactor based on CRP and RQA[J]. Large Electric Machine and Hydraulic Turbine, 2019(3):62-67.

LI Qingmin,ZHAO Tong,ZHANG Li,et al.Mechanical fault diagnostics of onload tap changer within power transformers based on hidden markov model[J]. IEEE Transactions on Power Delivery, 2012,27(2):596-601.

HUANG Weidi,GAN Chunbiao.A vector angle method of rolling bearing fault classification by phase-space reconstruction technique[J]. Journal of Testing and Evaluation,2020,48(4):2624-2638.

王泽峰,许辉群,杨梦琼,等.应用时域卷积神经网络的地震波阻抗反演方法[J].石油地球物理勘探,2022,57(2):279-286.

WANG Zefeng, XU Huiqun, YANG Mengqiong, et al.Seismic impedance inversion method based on time-domain convolutional neural network[J]. Oil Geophysical Prospecting , 2022 , 57(2):279-286.

林琳,陈志英.基于粗糙集神经网络和振动信号的高压断路器机械故障诊断[J].电工技术学报,2020,35(s1):277-283.

LIN Lin, CHEN Zhiying.Mechanical fault diagnosis of high voltage circuit breakers based on rough set neural networks and vibration signals[J]. Transactions of China Electrotechnical Society, 2020, 35 (s1):277-283.

DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531-544.

叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31):102-108.

YE Lin, LIU Peng.Combined model based on EMD-SVM for shortterm wind power prediction[J].Proceedings of the CSEE, 2011, 31 (31):102-108.

朱丽莉,杨志鹏,袁华.粒子群优化算法分析及研究进展[J].计算机工程与应用,2007,43(5):24-27.

ZHU Lili, YANG Zhipeng, YUAN Hua.Analysis and development of particle swarm optimization[J]. Computer Engineering and Applications, 2007, 43(5):24-27.

谢平,江国乾,武鑫,等.基于多尺度熵和距离评估的滚动轴承故障诊断[J].计量学报,2013,34(6):548-553.

XIE Ping, JIANG Guoqian, WU Xin, et al.Rolling bearing fault diagnosis based on multiscale entropy and distance evaluation[J]. Acta Metrologica Sinica, 2013, 34(6):548-553.

刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.

LIU Changliang, WU Yingjie, ZHEN Chenggang.Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J].Proceedings of the CSEE, 2015, 35(13):3358-3365.

唐贵基,王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(5):73-81.

TANG Guiji, WANG Xiaolong.Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi’an Jiaotong University, 2015, 49(5):73-81.

PANDEY A,WANG Deliang.TCNN:Temporal convolutional neu ral network for real-time speech enhancement in the time domain[C]//IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP). Brighton,UK:IEEE,2019:6875-6879.

FARHA Y A,GALL J.MS-TCN:Multi-stage temporal convolutional network for action segmentation[C]//Computer Vision & Pattern Recognition(CVPR).Long Beach,USA: IEEE,2019:3570-3579.

刘树鑫,宋健,刘洋,等.交流接触器触头系统运动分析及故障诊断研究[J].电工技术学报,2021,36(s2):477-486.

LIU Shuxin, SONG Jian, LIU Yang, et al.Research on motion analysis and fault diagnosis of contact system of AC contactor[J]. Transactions of China Electrotechnical Society, 2021, 36(s2):477-486.

向玲,邓泽奇,赵玥.基于LPF-VMD和KELM的风速多步预测模型[J].电网技术,2019,43(12):4461-4467.

XIANG Ling, DENG Zeqi, ZHAO Yue.Multi-step wind speed prediction model based on LPF-VMD and KELM[J]. Power System Technology, 2019, 43(12):4461-4467.

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