自适应小波阈值去噪算法用于局部放电白噪声去噪

De-noising for White Noise in Partial Discharge Signals by Adaptive Wavelet Threshold Estimation

为增强局部放电信号白噪声去噪算法的自适应能力,文中提出了一种自适应选择小波、分解层数与阈值的局部放电去噪算法。首先,在对信号进行逐层小波分解的基础上,以近似系数能量占比表征小波的能量特点,以小波能量熵表征信号波形分布特点,以二者比值作为每层分解最优小波的评价指标,选取比值最高的小波为最优小波,兼顾了信号的能量特点与波形分布特点;其次,对细节系数进行噪声白化检验,当细节系数不满足白化标准时停止分解,自适应地选择分解层数,降低了去噪误差;最后,用鲸鱼优化算法优化了阈值选取过程,同时提高了阈值精度与运算速度。为验证去噪效果,引入粒子群优化小波阈值去噪算法与基于尺度能量最大原则选取最优小波去噪算法,与文中算法进行对比。对局部放电仿真信号与实测信号的去噪结果表明,文中算法在不同信噪比下去噪的均方误差与幅值误差均最小,具有良好的应用价值。

For improving the adaptability of white noise de-noising algorithm,this paper presents an approach of choosing wavelet,level and threshold adaptively for de-noising of partial discharge(PD)signals.Firstly,on the basis of layer-by-layer wavelet decomposition of signals,energy percentage of approximate coefficients in all coefficients and wavelet energy entropy are calculated to represent the energy characteristics and shape characteristics.And the wavelet with maximum ratio will be selected.The energy and shape characteristics are taken into account simultaneously.Secondly,the algorithm applies white noise detection principle on detail coefficients of each level to get adaptive decomposition level.The decomposition will be terminated when the detail coefficients cannot meet white noise standards and the error will be reduced.Finally,the algorithm uses the whale optimization algorithm to optimise the process of getting adaptive threshold,improving the accuracy and speed.For verifying the de-noising results,PSO algorithm and maximum signal energy algorithm are used for de-nosing simulative PD signals and real PD signal.The results show that the algorithm proposed in this paper has the lest MSE and amplitude error and has a promising prospect in practical application.