High impedance fault detection in distribution network based on S-transform and average singular entropy

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.

Recognition of Hybrid PQ Disturbances Based on Multi-Resolution S-Transform and Decision Tree

Aiming at the problems of multiple types of power quality composite disturbances,strong feature correlation and high recognition error rate,a method of power quality composite disturbances identification based on multiresolution S-transform and decision tree was proposed.Firstly,according to IEEE standard,the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given,and the disturbance waveform samples are generated in batches.Then,in order to improve the recognition accuracy,the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis.On this basis,five disturbance time-frequency domain features are extracted,which quantitatively reflect the characteristics of the analyzed power quality disturbance signal,which is less than the traditional method based on S-transform.Finally,three classifiers such as K-nearest neighbor,support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances.Simulation results showthat the classification accuracy of decision tree algorithmis higher than that of K-nearest neighbor and support vector machine.Finally,the proposed method is compared with other commonly used recognition algorithms.Experimental results show that the proposedmethod is effective in terms of detection accuracy,especially for combined PQ interference.

Enhancing the resolution of seismic data based on the generalized S-transform

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.

Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals

Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to remove the ocular artifacts by traditional filtering methods. This paper proposes a new approach of artifact removal using S-transform (ST). It provides an instantaneous time-frequency repre-sentation of a time-varying signal and generates high magnitude S-coefficients at the instances of abrupt changes in the signal. A threshold function has been defined in S-domain to detect the artifact zone in the signal. The artifact has been attenuated by a suitable multiplying factor. The major advantage of ST-fil- tering is that the artifacts may be removed within a narrow time-window, while preserving the frequency information at all other time points. It also preserves the absolutely referenced phase information of the signal after the removal of artifacts. Finally, a com-parative study with wavelet transform (WT) and in-dependent component analysis (ICA) demonstrates the effectiveness of the proposed approach.

A Protection Method of VSC-HVDC Cables Based on Generalized S-Transform

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.

S-transformation based integrated approach for spectrum estimation, storage, and sensing in cognitive radio

Cognitive Radio (CR) uses the principle of dynamic spectrum allocation to improve the utilization of spectrum bands. The estimation of missing data is essential for maintaining an uninterrupted quality of service in the CR. However, the existing methods are not suitable for interpolating missing data in high frequency signals. The storage of spectrum occupancy information is crucial for learning the spectrum usage and prediction. The existing techniques for wideband spectrum sensing suffer from poor edge detection capabilities. This paper proposes an STransformation (ST) based approach to solve these problems. For missing samples, the proposed method improves the accuracy of estimation. The ST can also be used to store the spectrum occupancy information. The simulation results show that the proposed scheme outperforms others by improving the accuracy of edge detection. Further, the simple implementation of the ST in the frequency domain is an advantage for the real time application.

Characteristic Analysis of White Gaussian Noise in S-Transformation Domain

The characteristic property of white Gaussian noise (WGN) is derived in S-transformation domain. The results show that the distribution of normalized S-spectrum of WGN follows X2?distribution with two degrees of freedom. The conclusion has been confirmed through both theoretical derivations and numerical simulations. Combined with different criteria, an effective signal detection in S-transformation can be realized.

Power Supply Quality Analysis Using S-Transform and SVM Classifier

In this paper, a SVM classifier based on S-Transform is presented for power quality disturbances classification. Firstly, seven types of PQ events are created using Matlab simulation. These signals are analyzed to detect and localize PQ events via S-Transform by visual inspection. Then five significant features of the PQ disturbances are extracted from the S-Transform output. Afterwards, PQ disturbance samples with the five features are fed to SVM for training and automatic classification. Besides, particle swarm optimization is implemented to improve the performance of SVM. The results of the classification indicate that SVM classifier is an effective mechanism to detect and classify power quality disturbances.

Application of S-transform threshold filtering in Anhui experiment airgun sounding data de-noising

As a relatively new method of processing non-stationary signal with high time-frequency resolution, S transform can be used to analyze the time-frequency characteristics of seismic signals. It has the following characteristics: its time-frequency resolution corresponding to the signal frequency, reversible inverse transform, basic wavelet that does not have to meet the permit conditions. We combined the threshold method, proposed the S-transform threshold filtering on the basis of S transform timefrequency filtering, and processed airgun seismic records from temporary stations in "Yangtze Program"(the Anhui experiment). Compared with the results of the bandpass filtering, the S transform threshold filtering can improve the signal to noise ratio(SNR) of seismic waves and provide effective help for first arrival pickup and accurate travel time. The first arrival wave seismic phase can be traced farther continuously, and the Pm seismic phase in the subsequent zone is also highlighted.

基于S变换双阈值法的汽车零部件载荷谱加速编辑

针对S变换编辑法加速编辑效果不佳的问题,提出了一种基于S变换双阈值编辑方法。该方法先对载荷谱进行S变换以获取最大幅值谱,在以双阈值识别并保留的幅值谱片段为依据保留对应的载荷谱片段后,再将其拼接成加速后的载荷谱,最后对比分析S变换双阈值编辑法与S变换编辑法编辑的加速谱的统计参数、功率谱密度、穿级计数和疲劳仿真结果。研究结果表明,S变换双阈值编辑法可明显压缩原始载荷时间,且其压缩效率高于S变换编辑法,转向节的疲劳损伤和寿命分析误差更小,适用于汽车零部件载荷谱加速编辑研究。

天山中段2次6级地震前钻孔应变高频异常分析

2014年以来,天山中段分量钻孔应变仪空前增多,这些高采样率的应变观测资料蕴含着丰富的构造信息。如何从高采样率观测资料中提取有效的前兆异常信息,是分析研究人员亟待解决的问题。文章通过对天山中段分量钻孔应变观测数据进行S变换和超限率分析发现,在天山中段2次6级地震前有5套应变资料出现高频信息异常。这些异常均在震前出现,随后达到峰值,临震前或地震后衰减,其中短周期异常信号主要集中在10~720 min频段,且S变换与超限率分析结果具有很好的同步性。结合精河地震震源区及附近的GPS分析结果,发现高频异常信息的分布与该地区地壳运动场具有很好的一致性,进一步验证了高频信息异常的可信度。

基于初始电流行波相位的多端混合直流线路单端保护方案

为了解决LCC-MMC型多端混合直流系统线路保护存在的问题,提出一种基于初始电流行波相位的多端混合直流线路单端保护方案。首先基于多端混合直流输电线路发生不同位置和类型的故障工况,推导直流线路入射电压行波的解析式并计算其高频段相位。其次结合线路边界行波折反射原理和测量波阻抗特性,提出利用初始电压行波相位的区内外识别判据,并根据雷击和短路故障的入射行波低频相位特性构建雷击干扰判据。在PSCAD/EMTDC中搭建昆柳龙多端混合直流系统模型进行验证,证明所提方案仅利用单端量信息即可满足故障判断和选线要求,满足速度性的同时具备一定的抗过渡电阻能力(500Ω)。

经验小波变换和改进S变换结合的电能质量检测与识别方法

为分析不确定干扰因素影响下的实际电力网络电能质量问题,提出一种经验小波变换(EWT)和改进S变换相结合的电能质量检测与识别方法。该方法一方面利用EWT联合归一化直接正交(NDQ)算法和奇异值分解(SVD)算法准确提取调幅-调频分量的频率、幅值和时间参数,另一方面考虑到EWT算法在高噪声环境下瞬时幅值波动的问题,引入改进S变换提取高噪声干扰下的电能质量扰动时频信息,最后,基于EWT和改进S变换提取的扰动特征向量,利用基于改进粒子群优化算法(IPSO)优化支持向量机(SVM)的电能质量扰动识别分类器实现扰动类型的精确识别。仿真和实验表明所提方法在复合扰动识别分类时平均识别准确率为93.23%,且能够准确识别4种实测扰动信号。

S-100β CysC和NF-κB对急性缺血性脑卒中患者静脉溶栓后出血转化的预测价值

目的 探讨急性缺血性脑卒中(AIS)外周血中S-100β、CysC和NF-κB水平对静脉溶栓后出血转化的影响及预测价值。方法 收集2019-03—2022-03接受溶栓治疗的AIS患者140例,根据溶栓后24 h是否发生出血转化(HT)将患者分为非HT组(n=112)和HT组(n=28)。比较2组一般临床资料,采用多因Logistic回归分析影响HT发生的危险因素,受试者工作特征(ROC)曲线评估S-100β、CysC和NF-κB预测HT发生的价值。结果 HT组与非HT组相比,患者年龄、发病至溶栓时间、房颤、TOAST分型、C反应蛋白、凝血酶原时间、S-100β、CysC、NF-κB、白质高信号和脑微出血等均有统计学差异(P<0.05)。Logistic多因素回归分析显示,房颤、S-100β、CysC和NF-κB为影响HT发生的危险因素。S-100β、CysC和NF-κB预测AIS患者静脉溶栓后出血转化的曲线下面积分别为0.915(0.902~0.923)、0.874(0.856~0.882)和0.789(0.771~0.796),均具有一定的预测价值。结论 S-100β、CysC和NF-κB为AIS患者静脉溶栓后HT发生的危险因素,对HT发生具有一定的预测价值。

市域视角下安徽省科技成果转化效率评价

科学分析评价科技成果转化效率不仅能够体现该地区科研创新水平,也可以反映自身的未来发展潜力,有助于带动区域经济的发展。该研究选取安徽省16个地级市2014—2019年的数据,基于数据包络分析法(DEA),对市域范围科技成果转化绩效进行测量分析。研究发现,安徽省科技成果转化整体上效率偏低,而从市域层面上看,科技成果转化效率并不均衡。

基于同步提取广义S变换的机械故障诊断方法研究

现有的同步提取变换(synchroextracting transform, SET)窗函数固定缺乏灵活性,在进行故障诊断时很难有效获取到高时频精度和高抗干扰性能的瞬时频率,针对此问题,结合广义S变换可以自适应调节窗函数宽度的优点,提出一种基于同步提取广义S变换(synchroextracting generalized Stransform, SEGST)的机械故障诊断方法。SEGST方法的特点在于将Rényi熵作为度量时频聚集性的标准,通过在高斯窗函数中引入2个尺度调节因子来选择参数的最佳值,对得到的广义S变换二维时频谱构造出同步提取算子来提取时频脊线处的时频系数,该算子能保留与信号的时变特征最相关的TF信息,剔除多余的模糊时频能量,从而得到高时频分辨率的时频能量特征。仿真结果表明,所提方法不论在时频分辨率方面,还是在噪声鲁棒性方面,都优于传统时频分析方法,并且保持了良好的重构性。最后,将所提方法应用于航空发动机高速滚动轴承故障诊断中,结果表明,该方法能够准确识别故障信号中的特征频率。

同步挤压S变换与τ⁃p变换联合的微地震信号消噪方法

微地震监测是指导页岩气开采水力压裂作业和评价压裂效果的常用手段。地面监测所采集的微地震信号能量弱、信噪比低,微地震事件识别困难,严重影响定位的准确性。针对低信噪比地面微地震监测资料,联合使用同步挤压S变换、谱分解和τ⁃p变换,提出一种新的消噪方法。首先对监测资料进行时差校正,将微地震信号的同相轴校平;之后使用同步挤压S变换对校平后的资料进行谱分解获取单频切片;再对每个单频切片进行τ⁃p变换,并根据τ⁃p变换的结果获取微地震信号位置;最后根据信号的位置在时频域完成消噪。实际低信噪比地面微地震监测数据的处理结果表明,新方法可以获得理想的消噪结果。

基于S变换的临汾水平摆倾斜观测数据年变信息提取及预测指标确定

临汾台水平摆多年来监测能力及资料的映震能力备受关注,为推进长周期破年变分析在临汾台水平摆资料的应用,本文利用一种基于S变换时频分析方法的破年变信息分离提取方法,采用双向非对称阈值策略,结合R值评分及Molchan图表法,构建了临汾水平摆东西测项破年变预测指标,给出了长周期信号变化(ANA)和短周期信号变化(ONA)对于不同震级档的最佳阈值、最佳预报范围、最佳预报时窗,预报效能R值均大于R0值,可信度高。其中ANA指标对于500 km以内的ML≥5级的预报效能最好。

基于同步提取增强广义S变换的柴油机气门性能退化状态评估

柴油机在运行过程中气门间隙逐渐增大,其状态会随气门性能退化而发生改变。针对传统状态评估方法难以对其气门性能退化状态进行准确评估的问题,提出基于同步提取增强广义S变换(Synchro Extracting Enhanced Generalized S-Transform,SEEGST)的柴油机气门性能退化状态评估方法。通过传感器采集反映柴油机状态的振动信号;为解决传统信号时频分析方法存在时频分辨率低、能量聚集性弱等问题,基于同步提取算法与广义S变换提出SEEGST时频分析方法,将振动信号转换为二维时频图;利用MLP-Mixer模型提取时频图像特征进行训练,实现柴油机状态评估。通过柴油机状态监测实验台开展气门性能退化实验,将所提方法与SSGST-MLPMixer、GST-MLPMixer、SEEGST-ViT、SEEGST-2DCNN、FFT spectrum-1DCNN 5种传统方法对比。实验结果表明:所提方法的整体评估准确率达到98.96%,可有效应用于柴油机气门性能退化状态评估领域,为开展柴油机气门性能退化状态评估提供一种新的思路。

基于广义S变换的光伏电站谐波和间谐波分析方法

针对S变换高斯窗函数形式固定不变的问题,引入高斯窗调节因子改进S变换,提出一种基于广义S变换的光伏电站谐波和间谐波分析方法。首先通过MATLAB/Simulink搭建光伏电站的仿真模型,在自然采样双极性SPWM调制方式下采集逆变器的三相输出电压;然后以A相电压为例,采用广义S变换对电压信号处理得到一个模时频矩阵;最后对该矩阵分析实现光伏电站谐波和间谐波参数的准确计算。仿真结果表明,本文方法的幅值计算误差最大仅为3.30×10^(-4)%,频率的平均计算误差为0%,远小于S变换幅值误差35.19%和频率误差2.39%;能满足光伏电站谐波和间谐波检测精度的需要。