When a high impedance fault(HIF)occurs in a distribution network,the detection efficiency of traditional protection devices is strongly limited by the weak fault information.In this study,a method based on S-transform...When a high impedance fault(HIF)occurs in a distribution network,the detection efficiency of traditional protection devices is strongly limited by the weak fault information.In this study,a method based on S-transform(ST)and average singular entropy(ASE)is proposed to identify HIFs.First,a wavelet packet transform(WPT)was applied to extract the feature frequency band.Thereafter,the ST was investigated in each half cycle.Afterwards,the obtained time-frequency matrix was denoised by singular value decomposition(SVD),followed by the calculation of the ASE index.Finally,an appropriate threshold was selected to detect the HIFs.The advantages of this method are the ability of fine band division,adaptive time-frequency transformation,and quantitative expression of signal complexity.The performance of the proposed method was verified by simulated and field data,and further analysis revealed that it could still achieve good results under different conditions.展开更多
Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic ...Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration.展开更多
In view of the fact that the wavelet packet transform(WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component anal...In view of the fact that the wavelet packet transform(WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis(PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current(HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data,and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error(SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms,without being affected by the sampling frequency.展开更多
Surface electromyogram （EMG） signals were identified by fractal dimension.Two patterns of surface EMG signals were acquired from 30 healthy volunteers＇ right forearm flexor respectively in the process of forearm su...Surface electromyogram （EMG） signals were identified by fractal dimension.Two patterns of surface EMG signals were acquired from 30 healthy volunteers＇ right forearm flexor respectively in the process of forearm supination （FS） and forearm pronation （FP）.After the raw action surface EMG （ASEMG） signal was decomposed into several sub-signals with wavelet packet transform （WPT）,five fractal dimensions were respectively calculated from the raw signal and four sub-signals by the method based on fuzzy self-similarity.The results show that calculated from the sub-signal in the band 0 to 125 Hz,the fractal dimensions of FS ASEMG signals and FP ASEMG signals distributed in two different regions,and its error rate based on Bayes decision was no more than 2.26%.Therefore,the fractal dimension is an appropriate feature by which an FS ASEMG signal is distinguished from an FP ASEMG signal.展开更多
The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its ...The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its positioning accuracy degrades,or sometimes it even fails to work.Wavelet Packet Transform(WPT)is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers.But the conventional method suffers from threshold drifting and employs inflexible thresholding functions.So,to address these issues,an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding(IPSO-PWPT)is proposed.Firstly,a new parameter adaptive thresholding function is constructed.Then,a new form of inertia weight is presented to enhance the performance of PSO.Later,IPSO is used to optimize the key parameters of WPT.Finally,the implementation of the IPSO-PWPT anti-jamming algorithm is discussed.The performance of the proposed technique is evaluated for various performance metrics in four jamming environments.The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability.Also,the results show the ability of the new thresholding function to process various signals effectively.Furthermore,the findings reveal that the improved PSO outperforms the variants of PSO.展开更多
数字音乐指纹提取的主要目的是建立一种有效机制,用于比较2个音乐文件的听觉质量。提出一种基于小波包最优基分解的音乐指纹提取算法,利用与音频内容密切相关的小波包系数,将其作为特征进行指纹提取。实验结果表明,该算法对MP3,WMA和RM...数字音乐指纹提取的主要目的是建立一种有效机制,用于比较2个音乐文件的听觉质量。提出一种基于小波包最优基分解的音乐指纹提取算法,利用与音频内容密切相关的小波包系数,将其作为特征进行指纹提取。实验结果表明,该算法对MP3,WMA和RM压缩、噪声、Stirmark for audio工具中常见的音频信号处理具有强鲁棒性,且在不同音乐之间具有较高可区分性。展开更多
基金financial supported by the Natural Science Foundation of Fujian,China(2021J01633).
文摘When a high impedance fault(HIF)occurs in a distribution network,the detection efficiency of traditional protection devices is strongly limited by the weak fault information.In this study,a method based on S-transform(ST)and average singular entropy(ASE)is proposed to identify HIFs.First,a wavelet packet transform(WPT)was applied to extract the feature frequency band.Thereafter,the ST was investigated in each half cycle.Afterwards,the obtained time-frequency matrix was denoised by singular value decomposition(SVD),followed by the calculation of the ASE index.Finally,an appropriate threshold was selected to detect the HIFs.The advantages of this method are the ability of fine band division,adaptive time-frequency transformation,and quantitative expression of signal complexity.The performance of the proposed method was verified by simulated and field data,and further analysis revealed that it could still achieve good results under different conditions.
基金National Key Research and Development Program of China under Grant No.2023YFE0102900National Natural Science Foundation of China under Grant Nos.52378506 and 52208164。
文摘Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration.
基金supported by the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid (No. U2066210)。
文摘In view of the fact that the wavelet packet transform(WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis(PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current(HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data,and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error(SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms,without being affected by the sampling frequency.
基金The National Natural Science Foundation of China(No.60171006)the National Basic Research Programof China (973 Pro-gram) (No.2005CB724303).
文摘Surface electromyogram （EMG） signals were identified by fractal dimension.Two patterns of surface EMG signals were acquired from 30 healthy volunteers＇ right forearm flexor respectively in the process of forearm supination （FS） and forearm pronation （FP）.After the raw action surface EMG （ASEMG） signal was decomposed into several sub-signals with wavelet packet transform （WPT）,five fractal dimensions were respectively calculated from the raw signal and four sub-signals by the method based on fuzzy self-similarity.The results show that calculated from the sub-signal in the band 0 to 125 Hz,the fractal dimensions of FS ASEMG signals and FP ASEMG signals distributed in two different regions,and its error rate based on Bayes decision was no more than 2.26%.Therefore,the fractal dimension is an appropriate feature by which an FS ASEMG signal is distinguished from an FP ASEMG signal.
文摘The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its positioning accuracy degrades,or sometimes it even fails to work.Wavelet Packet Transform(WPT)is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers.But the conventional method suffers from threshold drifting and employs inflexible thresholding functions.So,to address these issues,an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding(IPSO-PWPT)is proposed.Firstly,a new parameter adaptive thresholding function is constructed.Then,a new form of inertia weight is presented to enhance the performance of PSO.Later,IPSO is used to optimize the key parameters of WPT.Finally,the implementation of the IPSO-PWPT anti-jamming algorithm is discussed.The performance of the proposed technique is evaluated for various performance metrics in four jamming environments.The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability.Also,the results show the ability of the new thresholding function to process various signals effectively.Furthermore,the findings reveal that the improved PSO outperforms the variants of PSO.
文摘数字音乐指纹提取的主要目的是建立一种有效机制,用于比较2个音乐文件的听觉质量。提出一种基于小波包最优基分解的音乐指纹提取算法,利用与音频内容密切相关的小波包系数,将其作为特征进行指纹提取。实验结果表明,该算法对MP3,WMA和RM压缩、噪声、Stirmark for audio工具中常见的音频信号处理具有强鲁棒性,且在不同音乐之间具有较高可区分性。
