时间差估计是局部放电定位的重要一环,现有算法受限于采样设备采样率,会产生无法忽略的系统误差,进而使定位误差过大。为了解决上述问题,该文提出了超分辨率最小均方广义互相关(super resolution least mean square generalized cross correlation,SR-LMS-GCC)时间差估计算法,使时间差估计的结果突破采样率的限制,能更精确地估算两信号之间的时间差。首先对离散时间信号的广义互相关进行改进,通过频率调整扩展了频谱,能够有效提高时间差估计的时间分辨率。为解决真实时间差溢出时间差估计区间的问题,提出了多级交叉互相关器的框架,并与自适应广义互相关时间差估计方法结合起来,提出了SR-LMS-GCC算法,并通过仿真讨论了算法参数的设置规则。最后通过实验验证,SR-LMS-GCC算法在时间差估计和定位准确度方面的比传统的受分辨率限制的算法均提升了85%以上。
To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ensemble empirical mode decomposition(EEMD)and cross-correlation algorithm was proposed.Firstly,a fast Fourier transform(FFT)spectrum analysis was utilized to ascertain the frequency range of the signal.Secondly,data acquisition was conducted at an appropriate sampling frequency,and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions(IMFs)by EEMD.Subsequently,these decomposed IMFs were recombined based on their energy entropy,and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering.Finally,an ideal ultrasonic Doppler flow rate signal was extracted.Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio(SNR)and extend the lower limit of measurement of the ultrasonic Doppler flow meter.
