基于波形匹配端点延拓法优化的经验模态分解算法在铁路继电器参数降噪上的应用

doi:10.19595/j.cnki.1000-6753.tces.201491

摘要
图/表
参考文献
相关文章 (1)

全文: PDF (1991 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要针对铁路继电器参数的噪声问题,为提取其有效信息,该文建立一种基于改进波形匹配延拓法优化的经验模态分解（EMD）算法,利用自相关函数分离含噪信号,小波阈值去除噪声的混合降噪模型。首先根据继电器参数特点对波形匹配方法进行改进,重新定义匹配误差度公式,并引入匹配精度误差系数,采用改进波形匹配延拓法优化EMD分解过程产生的端点效应,得到有效的固有模态分量（IMF）和余项;然后求解其自相关函数,并根据自相关函数图像结合噪声信号特征分离出含噪分量;最后对含噪分量进行小波阈值去噪,去噪后与剩余分量和余项结合,得到重构后的参数序列。同时,提出利用结构相似性（SSIM）评价指标,结合信噪比（SNR）、方均误差（MSE）指数对模型可靠度评判。通过结果分析,并与EMD分解后重构和小波阈值去噪方法作对比,证明该模型可优化铁路继电器参数的降噪效果。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李文华
	姜惠
	赵正元
	潘如政
	胡康生

关键词 ：端点效应, 波形匹配, 端点延拓法, 自相关函数, 结构相似性（SSIM）指数

Abstract：Aiming at the noise problem of railway relay parameters,this paper establishes a hybrid noise reduction model based on empirical mode decomposition algorithm optimized by the improved waveform matching continuation method to extract the effective information.This model uses autocorrelation function to separate noisy signals and wavelet threshold to remove noise. Firstly, according to the characteristics of relay parameters, the waveform matching method is improved.The matching error degree formula is redefined, and the matching accuracy error coefficient is introduced. The end effect produced by EMD decomposition process is optimized by the improved waveform matching continuation method, and the effective IMF component and residual term are obtained. Then, the autocorrelation function is solved, and the noise signal feature is separated according to the autocorrelation function image. Finally, the noisy component is denoised by wavelet threshold. After denoising, it is combined with the residual component and remainder to obtainthe reconstructed parameter sequence. Moreover, the SSIM index, SNR and MSE index are used to evaluate the reliability of the model. Compared with EMD reconstruction and the wavelet threshold de-noising method, the noise reduction effect of the model is proved.

Key words： Endpoint effect waveform matching endpoint continuation method autocorrelation function structural similarity index measurement (SSIM) index

收稿日期: 2020-11-14

PACS:

TM581

基金资助:河北省自然科学基金项目（E2020202221）和河北省自然科学基金创新群体项目（E2020202142）资助

通讯作者: 潘如政男,1977年生,副教授,研究方向为电器可靠性、高电压技术、放电等离子体应用等。E-mail: prz368@126.com

作者简介: 李文华男,1973年生,教授,研究方向为电器可靠性与寿命预测、储能技术及其可靠性。E-mail: liwenhua@hebut.edu.cn

引用本文:

李文华, 姜惠, 赵正元, 潘如政, 胡康生. 基于波形匹配端点延拓法优化的经验模态分解算法在铁路继电器参数降噪上的应用[J]. 电工技术学报, 2022, 37(10): 2656-2664. Li Wenhua, Jiang Hui, Zhao Zhengyuan, Pan Ruzheng, Hu Kangsheng. Application of Empirical Mode Decomposition Algorithm Based on Waveform Matching Endpoint Continuation Method in Noise Reduction of Railway Relay Parameters. Transactions of China Electrotechnical Society, 2022, 37(10): 2656-2664.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.201491 https://dgjsxb.ces-transaction.com/CN/Y2022/V37/I10/2656

[1] 苏伟龙, 许志红. 高压直流继电器磁吹系统的建模与设计[J]. 电工技术学报, 2022, 37(6): 1583-1594.
Su Weilong, Xu Zhihong.Modeling and design of magnetic blowing system for high voltage direct current relay[J]. Transactions of China Electro- technical Society, 2022, 37(6): 1583-1594.
[2] 李奎, 张国盼, 郑淑梅, 等. 基于实时服役参数的交流接触器电寿命最大化控制策略[J]. 电工技术学报, 2021, 36(9): 1976-1985.
Li Kui, Zhang Guopan, Zheng Shumei, et al.A control strategy for maximizing the electrical life of AC contactors based on real-time operating parame- ters[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1976-1985.
[3] Sun Biao, Zhou Shaoping, Wang Congyi.Application of wavelet soft threshold denoising algorithm based on EMD decomposition in vibration signals[C]//2019 6th International Conference on Systems and Infor- matics (ICSAI), Shanghai, China, 2019: 7-11.
[4] 陈琳, 陈静, 王惠民, 等. 基于小波包能量熵的电池剩余寿命预测[J]. 电工技术学报, 2020, 35(8): 1827-1835.
Chen Lin, Chen Jing, Wang Huimin, et al.Prediction of battery remaining useful life based on wavelet packet energy entropy[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1827-1835.
[5] 孙曙光, 庞毅, 王景芹, 等. 一种基于新型小波阈值去噪预处理的EEMD谐波检测方法[J]. 电力系统保护与控制, 2016, 44(2): 42-48.
Sun Shuguang, Pang Yi, Wang Jingqin, et al.EEMD harmonic detection method based on the new wavelet threshold denoising pretreatment[J]. Power System Protection and Control, 2016, 44(2): 42-48.
[6] 史林军, 周佳佳, 温荣超, 等. 基于经验模态分解与小波分析相结合的风电功率平滑控制[J]. 电力系统保护与控制, 2016, 44(24): 9-16.
Shi Linjun, Zhou Jiajia, Wen Rongchao, et al.Power smoothing control of wind power based on combination of empirical mode decomposition and wavelet analysis[J]. Power System Protection and Control, 2016, 44(24): 9-16.
[7] Liu Zhengkun, Zhang Ze.The improved algorithm of the EMD endpoint effect based on the mirror con- tinuation[C]//2016 Eighth International Conference on Measuring Technology and Mechatronics Auto- mation (ICMTMA), Macau, 2016: 792-795.
[8] 宿文才, 张树团, 刘陵顺. 基于改进极值波延拓的极点对称模态分解端点效应抑制方法[J]. 电工技术学报, 2020, 35(增刊1): 294-301.
Su Wencai, Zhang Shutuan, Liu Lingshun.Extreme- point symmetric mode decomposition endpoint reduction method based on improved extremal wave extension[J]. Transactions of China Electrotechnical Society, 2020, 35(S1): 294-301.
[9] 程启明, 陈路, 程尹曼, 等. 基于EEMD和LS-SVM模型的风电功率短期预测方法[J]. 电力自动化设备, 2018, 38(5): 27-35.
Cheng Qiming, Chen Lu, Cheng Yinman, et al.Short- term wind power forecasting method based on EEMD and LS-SVM model[J]. Electric Power Automation Equipment, 2018, 38(5): 27-35.
[10] 杨剑锋, 石戈戈, 周天奇, 等. 基于自适应序贯相似性检测波形匹配延拓的EMD端点效应抑制[J]. 振动与冲击, 2018, 37(18): 121-125.
Yang Jianfeng, Shi Gege, Zhou Tianqi, et al.Wave- form extension method based on similarity sequential detection for the end effects reduction of EMD[J]. Journal of Vibration and Shock, 2018, 37(18): 121-125.
[11] 胡明, 郭健鹏, 李富强, 等. 基于自适应CEEMD方法的电能质量扰动检测与分析[J]. 电力系统保护与控制, 2018, 46(21): 103-110.
Hu Ming, Guo Jianpeng, Li Fuqiang, et al.Power quality disturbance detection and analysis based on adaptively complementary ensemble empirical mode decomposition method[J]. Power System Protection and Control, 2018, 46(21): 103-110.
[12] 徐可, 陈宗海, 张陈斌, 等. 基于经验模态分解和支持向量机的滚动轴承故障诊断[J]. 控制理论与应用, 2019, 36(6): 915-922.
Xu Ke, Chen Zonghai, Zhang Chenbin, et al.Rolling bearing fault diagnosis based on empirical mode decomposition and support vector machine[J]. Control Theory & Applications, 2019, 36(6): 915-922.
[13] 李泽, 王辉, 钱勇, 等. 基于加速鲁棒特征的含噪局部放电模式识别[J]. 电工技术学报, 2022, 37(3): 775-785.
Li Ze, Wang Hui, Qian Yong, et al.Pattern recogni- tion of partial discharge in the presence of noise based on speeded up robust features[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 775-785.
[14] 马星河, 张登奎. 基于改进经验小波变换的高压电缆局部放电噪声抑制研究[J]. 电工技术学报, 2021, 36(增刊1): 353-361.
Ma Xinghe, Zhang Dengkui.Research on suppression of partial discharge noise of high voltage cable based on improved empirical wavelet transform[J]. Transa- ctions of China Electrotechnical Society, 2021, 36(S1): 353-361.
[15] Zhou Yufeng, Yu Mei, Ma Hualin, et al.Weighted-to- spherically-uniform SSIM objective quality evalu- ation for panoramic video[C]//2018 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China, 2018: 54-57.
[16] 肖贤贵, 李开成, 蔡得龙, 等. 一种电能质量扰动信号的联合去噪算法[J]. 电工技术学报, 2021, 36(21): 4418-4428.
Xiao Xiangui, Li Kaicheng, Cai Delong, et al.A combined de-noising method for power quality distur- bances events[J]. Transactions of China Electro- technical Society, 2021, 36(21): 4418-4428.
[17] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 6902—2010 铁路信号继电器试验方法[S]. 北京: 中国标准出版社, 2011.