高电压技术2017，Vol.43Issue(5)：1485-1492,8.DOI:10.13336/j.1003-6520.hve.20170428013
混合粒子群优化小波自适应阈值估计算法及用于局部放电去噪
Estimation Algorithm for Adaptive Threshold of Hybrid Particle Swarm Optimization Wavelet and Its Application in Partial Discharge Signals De-noising
摘要
Abstract
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.关键词
局部放电/在线监测/小波去噪/全局最优/类Sigmoid函数/自适应阈值/HPSOWATE算法Key words
partial discharge/online monitoring/wavelet de-noising/global optimum/differentiable Sigmoid function/adaptive thresholding/hybrid particle swarm optimization wavelet adaptive threshold estimation algorithm引用本文复制引用
李清泉,秦冰阳,司雯,王若茜,刘宾,马帅,王霞..混合粒子群优化小波自适应阈值估计算法及用于局部放电去噪[J].高电压技术,2017,43(5):1485-1492,8.基金项目
山东省科技发展计划(2014GGX104006).Project supported by Science and Technology Development Program of Shandong Province (2014GGX104006). （2014GGX104006）
