基于变分模态分解和凹凸型阈值小波的电缆局部放电信号降噪方法  被引量:12

Denoising Method of Cable Partial Discharge Signal Based on Variational Mode Decomposition and Concave-Convex Threshold Wavelet

在线阅读下载全文

作  者:吴昊 王东山 WU Hao;WANG Dongshan(School of Electronics and Information Engineering,Shanghai University of Electric Power,Yangpu District,Shanghai 200090,China;Beijing Smartchip Microeletronics Technology Company Limited,Changping District,Beijing 102200,China)

机构地区:[1]上海电力大学电子与信息工程学院,上海市杨浦区200090 [2]北京智芯微电子科技有限公司,北京市昌平区102200

出  处:《现代电力》2022年第5期579-586,共8页Modern Electric Power

基  金:国家重点研发计划项目(2018YFB2100200)。

摘  要:针对中高压电缆局部放电信号测量中常见的周期性窄带干扰和随机白噪声干扰的问题,提出了一种基于变分模态分解(variational mode decomposition,VMD)和凹凸型阈值小波变换相结合的局部放电信号的降噪方法。先通过VMD对原始信号进行分解重构完成初步的降噪,再通过凹凸型阈值的小波变换进行进一步降噪。应用此方法分别对仿真信号和实测信号进行噪声抑制,并与传统降噪方法的降噪效果进行对比。对比结果证明所提方法相较于传统的软硬阈值的小波降噪等方法有更好的降噪效果,局放信号特征保留效果也更好。In allusion to familiar periodic narrowband interference and random white noise interference during the measurement of partial discharge signals of medium and high voltage cables,based on variational mode decomposition(abbr.VMD)and concave-convex threshold wavelet a method to denoise cable partial discharge signal was proposed.Firstly,By means of VMD the original signal was decomposed and reconstructed to perform the preliminary noise reduction.Secondly,the wavelet transform of concave-convex threshold was utilized to conduct further noise reduction.Applying the proposed method the noise reduction for the simulation signal and the measured signal was carried out and the obtained results were compared with the effects by traditional noise reduction method.Comparison results show that the noise reduction effect of the proposed method is better than those of traditional noise reduction method such as soft and hard threshold wavelet noise reduction and so on,meanwhile,the reservation effect of the characteristic of local discharge signal is better.

关 键 词:局部放电 变模态分解 小波变换 改进阈值函数 降噪 

分 类 号:TM835[电气工程—高电压与绝缘技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象