粒子群优化小波自适应阈值法用于局部放电去噪

摘要

为了提高局部放电在线监测中小波去噪的自适应能力，并降低去噪信号的畸变率，提出了一种用于电力设备局部放电信号去噪的粒子群优化小波自适应阈值方法。该方法采用小波对局部放电信号进行分解，在阈值选择时采用基于SURE无偏估计的最优阈值自适应选择方法，结合粒子群优化算法进行全局自适应搜索最优阈值，使最优阈值自适应寻优速度大大提高。为了验证其去噪效果，还引入遗传算法对小波自适应阈值法进行优化计算。对局部放电仿真信号与实测局部放电信号的去噪结果表明，本文与标准软阈值法和遗传算法优化小波自适应阈值法相比，能更好地去除局部放电信号中的白噪声，计算速度更快，具有良好的去噪效果和应用价值。%For the purpose of improving adaptive performance of wavelet de-noising and reducing distortion of de-noised signal, this paper presents an approach of particle swarm optimization （PSO） adaptive wavelet threshold estimation （PSOTE） for de-noising of partial discharge （PD） signals. The wavelet de-nosing algorithm is based on an optimum and adaptive shrinkage scheme. A Class of shrinkage functions with continuous derivatives and PSO algorithm are utilized for the adaptive shrinkage scheme. The PSO algorithm is competent to obtain the global optimum thresholds and to raise the efficiency of adaptive searching computation. For verifying the de-noising results, genetic algorithm is adopted to optimize the wavelet threshold. The de-noising results of simulative PD signals and the field PD signals are presented. The results show that the white noise can be removed effectively by the PSOTE, the distortion of which is smaller than the signals de-noised by the standard soft threshold estimation （STE） and genetic adaptive wavelet threshold estimation （GTE）. Meanwhile, the PSOTE is a much less time-consuming scheme and exhibits a promising prospect in practical application.