Many dynamic signals of mining machines are transient, such as load signals when roadheader’s cutting head being cut-in or cut-out and response signals produced by these loads.For these transient signals, the traditi...Many dynamic signals of mining machines are transient, such as load signals when roadheader’s cutting head being cut-in or cut-out and response signals produced by these loads.For these transient signals, the traditional Fourier analysis method is quite inadequate.The limitations of analysis, resolution by using Short-Time Fourier Transform (STFT) on them were discussed in this paper. Because of wavelet transform having the characteristics of flexible window and multiresolution analysis, we try to apply it to analyse these transient signal. In order to give a pratical example,using D18 wavelet and Mallat’s tree algorithm with MATLAB, the discrete wavelet transform was calculated for the simulating response signals of a three-degree-of freedom vibration system when it Was under impulse and random excitations. The results of the wavelet transform made clear its effectiveness and superiority in analysing transient signals of mining machines.展开更多
Precise fault location plays an important role in the reliability of modern power systems.With the in-creasing penetration of renewable energy sources,the power system experiences a decrease in system inertia and alte...Precise fault location plays an important role in the reliability of modern power systems.With the in-creasing penetration of renewable energy sources,the power system experiences a decrease in system inertia and alterations in steady-state characteristics following a fault occurrence.Most existing single-ended phasor domain methods assume a certain impedance of the remote-end system or consistent current phases at both ends.These problems present challenges to the applicability of con-ventional phasor-domain location methods.This paper presents a novel single-ended time domain fault location method for single-phase-to-ground faults,one which fully considers the distributed parameters of the line model.The fitting of transient signals in the time domain is real-ized to extract the instantaneous amplitude and phase.Then,to eliminate the error caused by assumptions of lumped series resistance in the Bergeron model,an im-proved numerical derivation is presented for the distrib-uted parameter line model.The instantaneous symmet-rical components are extracted for decoupling and inverse transformation of three-phase recording data.Based on the above,the equation of instantaneous phase constraint is established to effectively identify the fault location.The proposed location method reduces the negative effects of fault resistance and the uncertainty of remote end pa-rameters when relying on one-terminal data for localiza-tion.Additionally,the proposed fault analysis methods have the ability to adapt to transient processes in power systems.Through comparisons with existing methods in three different systems,the fault position is correctly identified within an error of 1%.Also,the results are not affected by sampling rates,data windows,fault inception angles,and load conditions. Index Terms—Fault location,distributed parameter line model,transient signal,renewable energy,instantaneous phase.展开更多
Otoacoustic emissions (OAEs) has been considered as an excellent objective tool in clinics for diagnosing hearing loss. The signal-to-noise ratio (SNR) and correlation coefficient of OAEs are very important for the pu...Otoacoustic emissions (OAEs) has been considered as an excellent objective tool in clinics for diagnosing hearing loss. The signal-to-noise ratio (SNR) and correlation coefficient of OAEs are very important for the purpose of diagnosis. An adaptive signal enhancer (ASE) based on the Least Mean Square (LMS) algorithm is presented to detect transient evoked OAEs (TEOAEs). The TEOAEs detection results from 106 ears show that ASE reaches better estimation of TEOAEs than a conventional ensemble averaging (EA) technique. With the ASE, the improvement of SNR was increased faster than that with the EA and the number of sweeps required can be markedly reduced. The detection time with ASE could be shortened by about 50% in comparison with that of EA.展开更多
文摘Many dynamic signals of mining machines are transient, such as load signals when roadheader’s cutting head being cut-in or cut-out and response signals produced by these loads.For these transient signals, the traditional Fourier analysis method is quite inadequate.The limitations of analysis, resolution by using Short-Time Fourier Transform (STFT) on them were discussed in this paper. Because of wavelet transform having the characteristics of flexible window and multiresolution analysis, we try to apply it to analyse these transient signal. In order to give a pratical example,using D18 wavelet and Mallat’s tree algorithm with MATLAB, the discrete wavelet transform was calculated for the simulating response signals of a three-degree-of freedom vibration system when it Was under impulse and random excitations. The results of the wavelet transform made clear its effectiveness and superiority in analysing transient signals of mining machines.
文摘Precise fault location plays an important role in the reliability of modern power systems.With the in-creasing penetration of renewable energy sources,the power system experiences a decrease in system inertia and alterations in steady-state characteristics following a fault occurrence.Most existing single-ended phasor domain methods assume a certain impedance of the remote-end system or consistent current phases at both ends.These problems present challenges to the applicability of con-ventional phasor-domain location methods.This paper presents a novel single-ended time domain fault location method for single-phase-to-ground faults,one which fully considers the distributed parameters of the line model.The fitting of transient signals in the time domain is real-ized to extract the instantaneous amplitude and phase.Then,to eliminate the error caused by assumptions of lumped series resistance in the Bergeron model,an im-proved numerical derivation is presented for the distrib-uted parameter line model.The instantaneous symmet-rical components are extracted for decoupling and inverse transformation of three-phase recording data.Based on the above,the equation of instantaneous phase constraint is established to effectively identify the fault location.The proposed location method reduces the negative effects of fault resistance and the uncertainty of remote end pa-rameters when relying on one-terminal data for localiza-tion.Additionally,the proposed fault analysis methods have the ability to adapt to transient processes in power systems.Through comparisons with existing methods in three different systems,the fault position is correctly identified within an error of 1%.Also,the results are not affected by sampling rates,data windows,fault inception angles,and load conditions. Index Terms—Fault location,distributed parameter line model,transient signal,renewable energy,instantaneous phase.
基金This work was supported by the National Natural Science Foundation of China (No.39870212)
文摘Otoacoustic emissions (OAEs) has been considered as an excellent objective tool in clinics for diagnosing hearing loss. The signal-to-noise ratio (SNR) and correlation coefficient of OAEs are very important for the purpose of diagnosis. An adaptive signal enhancer (ASE) based on the Least Mean Square (LMS) algorithm is presented to detect transient evoked OAEs (TEOAEs). The TEOAEs detection results from 106 ears show that ASE reaches better estimation of TEOAEs than a conventional ensemble averaging (EA) technique. With the ASE, the improvement of SNR was increased faster than that with the EA and the number of sweeps required can be markedly reduced. The detection time with ASE could be shortened by about 50% in comparison with that of EA.
