一种水电机组尾水管压力脉动数据滤波方法

Filtering method for data of pressure fluctuations in draft tube of hydropower units

尾水管压力信号的降噪处理对水电机组振动故障识别具有重要意义。为解决电站尾水管压力脉动数据中干扰信号对后续信号分析产生干扰的问题,本文利用自适应噪声完备集合经验模态分解将信号分解为若干个本征模态函数,并利用改进后的最大信息系数对各本征模态函数与原信号进行相关性计算,根据相关性系数的大小划定阈值,最后将处理后的本征模态函数叠加,用来压力信号的重构。并通过实例分析验证了该方法的有效性,同时与经验模态分解和传统的最大信息系数作对比,结果表明,本文提出的自适应噪声完备集合经验模态分解和改进的最大信息系数方法在水电机组压力数据的净化方面具更高的可信度。

Noise reduction processing of the draft tube pressure signals is of great significance for vibration fault identification of hydropower units;a key issue for a power plant is to process the interference component of such signals to eliminate its effect in subsequent signal analysis.To address the issue,this study used the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMD-AN)to decompose the signal into several Intrinsic Mode Functions,and used the Improved Maximum Information Coefficient(IMIC)to calculate the correlation between each intrinsic mode function and the original signal.A threshold value is determined based on the correlation coefficient calculated,and finally the intrinsic mode functions were processed and superimposed to reconstruct the pressure signal.The efficacy of this method was verified using an example analysis,and compared with Empirical Mode Decomposition and the traditional Maximum Information Coefficient method.The results show our new method IMIC-CEEMDAN has higher reliability in the purification of pressure data for hydropower units.