In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as ...In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as in the conventional resolution-enhanced techniques, the wavelet which changes with time and frequency was simulated and eliminated. After using the inverse S-transform for the processed instantaneous spectrum, the signal in the time domain was obtained again with a more balanced spectrum and broader frequency band. The quality of seismic data was improved without additional noise.展开更多
<div style="text-align:justify;"> Generalized S-transform is a time-frequency analysis method which has higher resolution than S-transform. It can precisely extract the time-amplitude characteristics o...<div style="text-align:justify;"> Generalized S-transform is a time-frequency analysis method which has higher resolution than S-transform. It can precisely extract the time-amplitude characteristics of different frequency components in the signal. In this paper, a novel protection method for VSC-HVDC (Voltage source converter based high voltage DC) based on Generalized S-transform is proposed. Firstly, extracting frequency component of fault current by Generalized S-transform and using mutation point of high frequency to determine the fault time. Secondly, using the zero-frequency component of fault current to eliminate disturbances. Finally, the polarity of sudden change currents in the two terminals is employed to discriminate the internal and external faults. Simulations in PSCAD/EMTDC and MATLAB show that the proposed method can distinguish faults accurately and effectively. </div>展开更多
The echo of the material level is non-stationary and contains many singularities.The echo contains false echoes and noise,which affects the detection of the material level signals,resulting in low accuracy of material...The echo of the material level is non-stationary and contains many singularities.The echo contains false echoes and noise,which affects the detection of the material level signals,resulting in low accuracy of material level measurement.A new method for detecting and correcting the material level signal is proposed,which is based on the generalized S-transform and singular value decomposition(GST-SVD).In this project,the change of material level is regarded as the low speed moving target.First,the generalized S-transform is performed on the echo signals.During the transformation process,the variation trend of window of the generalized S-transform is adjusted according to the frequency distribution characteristics of the material level echo signal,achieving the purpose of detecting the signal.Secondly,the SVD is used to reconstruct the time-frequency coefficient matrix.At last,the reconstructed time-frequency matrix performs an inverse transform.The experimental results show that the method can accurately detect the material level echo signal,and it can reserve the detailed characteristics of the signal while suppressing the noise,and reduce the false echo interference.Compared with other methods,the material level measurement error does not exceed 4.01%,and the material level measurement accuracy can reach 0.40%F.S.展开更多
为实现电力系统次/超同步振荡的快速、准确辨识,提出了一种基于同步压缩广义S变换(synchrosqueezing generalized S transform, SSGST)和改进稀疏时域法(improved sparse time domain method,ISTD)结合的次/超同步振荡辨识方法。该方法...为实现电力系统次/超同步振荡的快速、准确辨识,提出了一种基于同步压缩广义S变换(synchrosqueezing generalized S transform, SSGST)和改进稀疏时域法(improved sparse time domain method,ISTD)结合的次/超同步振荡辨识方法。该方法首先利用能量比函数对电力系统广域量测信息实时检测,当检测到信号能量发生突变时,利用SSGST对检测到的振荡信号分解得到相应的SSGST时频系数矩阵;然后通过改进的脊线提取方法在时频域实现对各振荡分量的最优轨迹搜索;进一步,结合最优轨迹时频索引重构各振荡分量的时域分量,并利用ISTD辨识方法计算出各振荡分量的频率和阻尼比系数;最后,通过自合成模拟信号、双馈风电场经串补并网系统仿真信号和某实际风电场实测数据验证了所提方法的准确性和有效性。展开更多
Banach space-valued generalized functionals of white noise form an important part of vector-valued generalized functionals of white noise. In this paper, we discuss the differential of abstract function valued in B-va...Banach space-valued generalized functionals of white noise form an important part of vector-valued generalized functionals of white noise. In this paper, we discuss the differential of abstract function valued in B-valued generalized functional space. A characterized theorem is obtained by using their S-transform.展开更多
基金supported by National 973 Key Basic Research Development Program(No.2007CB209602)National 863 High Technology Research Development Program(No.2007AA067.229)
文摘In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as in the conventional resolution-enhanced techniques, the wavelet which changes with time and frequency was simulated and eliminated. After using the inverse S-transform for the processed instantaneous spectrum, the signal in the time domain was obtained again with a more balanced spectrum and broader frequency band. The quality of seismic data was improved without additional noise.
文摘<div style="text-align:justify;"> Generalized S-transform is a time-frequency analysis method which has higher resolution than S-transform. It can precisely extract the time-amplitude characteristics of different frequency components in the signal. In this paper, a novel protection method for VSC-HVDC (Voltage source converter based high voltage DC) based on Generalized S-transform is proposed. Firstly, extracting frequency component of fault current by Generalized S-transform and using mutation point of high frequency to determine the fault time. Secondly, using the zero-frequency component of fault current to eliminate disturbances. Finally, the polarity of sudden change currents in the two terminals is employed to discriminate the internal and external faults. Simulations in PSCAD/EMTDC and MATLAB show that the proposed method can distinguish faults accurately and effectively. </div>
基金National Natural Science Foundation of China(No.61761027)。
文摘The echo of the material level is non-stationary and contains many singularities.The echo contains false echoes and noise,which affects the detection of the material level signals,resulting in low accuracy of material level measurement.A new method for detecting and correcting the material level signal is proposed,which is based on the generalized S-transform and singular value decomposition(GST-SVD).In this project,the change of material level is regarded as the low speed moving target.First,the generalized S-transform is performed on the echo signals.During the transformation process,the variation trend of window of the generalized S-transform is adjusted according to the frequency distribution characteristics of the material level echo signal,achieving the purpose of detecting the signal.Secondly,the SVD is used to reconstruct the time-frequency coefficient matrix.At last,the reconstructed time-frequency matrix performs an inverse transform.The experimental results show that the method can accurately detect the material level echo signal,and it can reserve the detailed characteristics of the signal while suppressing the noise,and reduce the false echo interference.Compared with other methods,the material level measurement error does not exceed 4.01%,and the material level measurement accuracy can reach 0.40%F.S.
文摘为实现电力系统次/超同步振荡的快速、准确辨识,提出了一种基于同步压缩广义S变换(synchrosqueezing generalized S transform, SSGST)和改进稀疏时域法(improved sparse time domain method,ISTD)结合的次/超同步振荡辨识方法。该方法首先利用能量比函数对电力系统广域量测信息实时检测,当检测到信号能量发生突变时,利用SSGST对检测到的振荡信号分解得到相应的SSGST时频系数矩阵;然后通过改进的脊线提取方法在时频域实现对各振荡分量的最优轨迹搜索;进一步,结合最优轨迹时频索引重构各振荡分量的时域分量,并利用ISTD辨识方法计算出各振荡分量的频率和阻尼比系数;最后,通过自合成模拟信号、双馈风电场经串补并网系统仿真信号和某实际风电场实测数据验证了所提方法的准确性和有效性。
文摘Banach space-valued generalized functionals of white noise form an important part of vector-valued generalized functionals of white noise. In this paper, we discuss the differential of abstract function valued in B-valued generalized functional space. A characterized theorem is obtained by using their S-transform.
