标题:Estimation Algorithm for Adaptive Threshold of Hybrid Particle Swarm Optimization Wavelet and Its Application in Partial Discharge Signals De-noising
作者:Li, Qingquan ;Qin, Bingyang ;Si, Wen ;Wang, Ruoxi ;Liu, Bin ;Ma, Shuai ;Wang, Xia
作者机构:[Li, Qingquan ;Qin, Bingyang ;Si, Wen ] Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment (School of Electrical Engineer 更多
通讯作者:Li, Qingquan
来源:Gaodianya Jishu/High Voltage Engineering
出版年:2017
卷:43
期:5
页码:1485-1492
DOI:10.13336/j.1003-6520.hve.20170428013
关键词:Adaptive thresholding; Differentiable Sigmoid function; Global optimum; Hybrid particle swarm optimization wavelet adaptive threshold estimation algorithm; Online monitoring; Partial discharge; Wavelet de-noising
摘要:Wavelet de-noising is a common de-noising method used in partial discharge (PD) detection, threshold estimation is closely related to distortion and error of partial discharge signals. For the purpose of improving the adaptive performances of wavelet de-noising and reducing distortion of de-noised signal, we put forward an approach of hybrid particle swarm optimization wavelet adaptive threshold estimation (HPSOWATE) for de-noising of partial discharge signals. To solve the premature convergence problem of common threshold selection methods, the HPSOWATE algorithm merging crossover mutation and chaos was proposed to obtain the global optimum thresholds. Genetic algorithm and particle swarm algorithm were adopted to optimize the wavelet threshold. Moreover,the de-noising results of simulative PD signals and the field PD signals were presented. The results show that HPSOWATE has a fast convergence rate and effective global optimization ability than the others and can significantly improve the credibility of the results and algorithm calculation speed. This HPSOWATE gives better mean square error (MSE) and amplitude error performance of de-noising effects, can remove the white noise effectively, and has good value in practical PD online monitoring. © 2017, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026814611&doi=10.13336%2fj.1003-6520.hve.20170428013&partnerID=40&md5=ff13c09269d7985735adb866444ab34d
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