基于改进VMD去噪的Prony-GSO联合谐波检测方法  被引量:8

Prony-GSO combined harmonic detection method based on VMD de-noising

在线阅读下载全文

作  者:王梦昊 李开成[1] 刘畅 王伟 陈西亚[1] Wang Menghao;Li Kaicheng;Liu Chang;Wang Wei;Chen Xiya(School of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan 100080,China;State Grid Changzhou Power Supply Company,Changzhou 213000,Jiangsu,China)

机构地区:[1]华中科技大学电气与电子工程学院,武汉100080 [2]国家电网常州供电公司,江苏常州213000

出  处:《电测与仪表》2020年第24期101-107,共7页Electrical Measurement & Instrumentation

基  金:国家自然科学基金资助项目(52077089)。

摘  要:针对传统Prony谐波检测方法在噪声条件下存在局限性,提出了一种能够在较大噪声条件下准确进行谐波检测的方法。通过对采样信号进行自适应的VMD分解,利用平均周期能量进行噪声模态的选择并予以剔除,将剩余模态重组后得到适合Prony算法的平稳信号;对平稳信号进行Prony谐波分析得到初步的谐波特征信息;对谐波特征信息进行循环筛选与GSO寻优,得到最终的谐波与间谐波特征信息。利用该方法进行谐波检测仿真实验,仿真结果表明,该方法有效提高了Prony算法在较大噪声条件下的辨识准确度,具有自适应性,能够自动筛选真实频率成分,具有高效、精确等优点。Aiming at the limitation of the traditional Prony harmonic detection method under noise conditions,this paper proposes a strategy for accurate harmonic detection under large noise conditions.By adaptive VMD decomposition of the sampled signal,we utilize the average periodic energy to select and reject the noise mode,and recombine the residual modes to obtain a stationary signal suitable for the Prony algorithm,and then,Prony harmonic analysis of the stationary signal is carried out to obtain the preliminary harmonic characteristic information.Finally,cyclic filtering and GSO optimization are carried out to obtain the final harmonic and inter-harmonic characteristic information.This method is utilized to carry out harmonic detection simulation experiment.The simulation results show that the proposed method can effectively improve the recognition accuracy of the Prony algorithm under large noise conditions.It is adaptive and can automatically filter the real frequency components.It is also efficient and accurate.

关 键 词:VMD去噪 PRONY算法 GSO 谐波检测 

分 类 号:TM933[电气工程—电力电子与电力传动]

 

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