基于APSO-SSD-SVD的特高压换流站OLTC振动信号降噪方法

Noise reduction method for the OLTC vibration signal of a UHV converter stationbased on APSO-SSD-SVD

随着中国特高压交直流换流站的大规模投运,有载分接开关(on-load tap changer, OLTC)已成为特高压换流站中发生故障较多的设备之一。针对强背景噪声环境下特高压换流站OLTC故障特征难以提取的问题,提出一种基于自适应粒子群算法优化奇异谱分解和奇异值分解的方法。首先,利用自适应粒子群优化(adaptive particle swarm optimization, APSO)算法对奇异谱分解算法中的模态参数进行优化,选取最优分解模态数。其次,基于最大峭度准则选取最佳奇异谱分量。然后,确定最佳重构阶数,通过奇异值分解重构信号,从而达到信号降噪的目的。将所提方法应用于仿真信号和实验信号,结果表明所提方法的信噪比达到23.302,均方根误差仅为0.004,并且波形相似参数高达0.998,优于其他降噪方法。所提方法能够更有效地实现对特高压换流站OLTC振动信号的降噪,为辅助运维人员诊断OLTC状态提供参考。

With the large-scale commissioning of UHV AC and DC converter stations in China,the on-load tap changer(OLTC)has become one of the devices with more faults in UHV converter stations.To address the problem of difficult extraction of OLTC fault features in UHV converter stations in a high background noise environment,this paper proposes a method based on an adaptive particle swarm algorithm to optimize singular spectrum and singular value decomposition.First,the modal parameters in the singular spectrum decomposition algorithm are optimized using the APSO algorithm,and the optimal number of decomposition modes is selected.Secondly,the optimal singular spectrum components are selected based on the maximum cliff criterion.Then,the optimal reconstruction order is determined,and the signal is reconstructed by singular value decomposition,so as to achieve the purpose of signal noise reduction.Applying the proposed method to the simulated and experimental signals,the results show that the proposed method achieves a signal-to-noise ratio of 23.302,the root-mean-square error is only 0.004,and the waveform similarity parameter is as high as 0.998.This is better than other noise reduction methods.The method proposed can more effectively achieve the noise reduction of OLTC vibration signals in UHV converter stations,and provides a reference for auxiliary operation and maintenance personnel to diagnose the OLTC status.