基于粒子群优化的最佳阈值法在局部放电信号去噪中应用

Application of the optimum threshold method based on particle swarm optimization to partial discharge signal de-noising

抑制白噪声干扰是局部放电(Partial Discharge,PD)在线检测中的关键技术。提出一种基于粒子群优化的最优阈值选取去噪方法。该方法采用小波对局部放电信号进行分解,在选取阈值时建立广义交叉验证准则,以广义交叉验证准则作为适应度值函数,并结合粒子群优化算法自适应地确定出各分解层的最佳阈值。该方法不依赖任何先验知识,实现局部放电信号自适应去噪。对局部放电仿真信号和实测局部放电信号的去噪结果表明:本文提出的方法与标准阈值法相比,能更好地去除局部放电信号中的白噪声。

The suppression of white noise interference is one of the key techniques in the on-line monitoring of partial discharge （ PD） .This paper proposed an optimal threshold de-noising method based on particle swarm optimization, which adopted wavelets to decompose the PD signals.When choosing the threshold, the generalized cross validation criterion was established and then was used as fitness function.By using the particle swarm optimization algorithm, the optimum threshold of every decomposition scale was adaptively determined.The threshold selection method, which does not rely on any prior knowledge, can realize adaptive de-noising of the PD signals.The de-noising results of the PD simulation signals and the field PD signals showed that compared with the standard threshold estimation method, the method proposed in this paper could remove the white noise in the PD signals more effectively.