基于改进LMS自适应滤波的局部放电噪声抑制方法

Partial Discharge Noise Suppression Method Based on Improved LMS Adaptive Filtering

为了解决局部放电测量现场中信号淹没在周期性窄带干扰中的问题,文中提出一种应用于变压器局部放电在线监测系统的改进变步长最小均方(least mean square,LMS)自适应滤波算法,通过构造一个新型滤波函数结合实际情况中AD芯片量程自适应调整步长,解决了传统LMS算法需要阶数和步长匹配、收敛性差、容易发散的缺点。通过改变滤波器阶数和参考信号时延,分析改进算法收敛速度及稳态误差,并对测试中发现时延为0的特殊情况进行了讨论分析,为高信噪比自适应滤波器设计提供了参考。改变新方法初始迭代步长同传统固定步长LMS算法的迭代过程进行了仿真对比,证明了新方法具有收敛速度快、不易发散的优点。最后,通过实验室搭建的变压器局放在线监测装置,对比分析了实测数据下传统LMS算法与本文算法的不同效果,通过对信噪比(SNR)、均方误差(MSE)和波形相似系数(NCC)三种指标对比,验证了新方法的优越性。

In order to solve the problem that the signal is submerged in periodic narrow-band interference in the partial discharge measurement site,an improved variable step size least mean square(LMS)adaptive filter algorithm applied to the transformer partial discharge online monitoring system was proposed.By constructing a new filter function combined with the actual situation of the AD chip range adaptive adjustment step size,it solved the traditional the LMS algorithm needs to match the order and step size,and had the disadvantages of poor convergence and easy divergence.The special case where the delay is found to be zero in the test is discussed and analyzed,which provides a high signal-to-noise ratio adaptive filter design.For reference.The change of the initial iteration step length of the new method is compared with the iteration process of the traditional fixed-step LMS algorithm,which proves that the new method has the advantages of fast convergence and not easy to diverge.Finally,through the transformer partial line monitoring device built in the laboratory,the different effects of the traditional LMS algorithm and the algorithm in this paper under the measured data are compared and analyzed,and the signal-to-noise ratio(SNR),mean square error(MSE)and waveform similarity coefficient(NCC)verifies the superiority of the new method.