利用S变换时频等值线图与幅值包络向量实现电压凹陷的分类

提出了一种基于S变换的电压凹陷分类方法.该方法基于各种电压凹陷在三相幅值凹凸性、凹陷持续阶段特定谐波成分以及幅值突变次数等方面的不同特征,通过应用具有良好时频分析能力的S变换提取这些特征,并采用修正一阶中心矩、时频等值线图、相位变化曲线等环节实现对电压凹陷的分类.分类过程简明,可对故障、故障自清除、感应电动机起动以及变压器励磁等多种原因引起的电压凹陷进行正确分类.仿真验证了该方法的有效性.     

基于S变换模时频矩阵相似度的短时电能质量扰动分类

提出了一种基于S变换模时频矩阵相似度的短时电能质量扰动分类方法。首先,建立各种扰动的标准模时频矩阵,然后计算扰动信号模时频矩阵与标准模时频矩阵的相似度,按照相似度最大的原则将扰动分类。该方法直接利用S变换结果,而不用增加其它算法或变换,原理简单、计算方便。仿真结果显示,该分类方法在不同噪声水平下均能达到满意的分类正确率,是一种有效的短时电能质量扰动分类方法。     

基于混沌集成决策树的电能质量复合扰动识别

电能质量复合扰动;扰动识别;混沌集成决策树;S变换     

A Fuzzy Rule Base Approach for Intelligent Protection of Microgrids

The proposed work develops a decision tree-induced fuzzy rule base intelligent protection scheme for fault detection and classification in a microgrid with multiple distributed generation interfaces. The proposed protection scheme retrieves one cycle post-fault current signal samples of each phase from fault inception at bus ends of the respective feeder to derive some differential features. The retrieved current samples are pre-processed using S-transform to obtain a time–frequency contour. The statistical features, such as energy, mean, standard deviation, and entropy, are computed from the time–frequency contour, which is further used to calculate the differential features. The differential features are used to build the fault classification tree. From the decision tree classification boundaries, the fuzzy membership functions are drawn, and further, the corresponding fuzzy rule base is generated for the final relaying decision. The proposed scheme is developed on a MATLAB/SIMULINK (The MathWorks, Natick, Massachusetts, USA) platform, including wide variations in faulted conditions, and the extensive test results indicate that the proposed intelligent relaying scheme can reliably provide protection measures for microgrids with different modes of operation.     

Combined VMD-SVM based feature selection method for classification of power quality events

Power quality (PQ) issues have become more important than before due to increased use of sensitive electrical loads. In this paper, a new hybrid algorithm is presented for PQ disturbances detection in electrical power systems. The proposed method is constructed based on four main steps: simulation of PQ events, extraction of features, selection of dominant features, and classification of selected features. By using two powerful signal processing tools, i.e. variational mode decomposition (VMD) and S-transform (ST), some potential features are extracted from different PQ events. VMD as a new tool decomposes signals into different modes and ST also analyzes signals in both time and frequency domains. In order to avoid large dimension of feature vector and obtain a detection scheme with optimum structure, sequential forward selection (SFS) and sequential backward selection (SBS) as wrapper based methods and Gram–Schmidt orthogonalization (GSO) based feature selection method as filter based method are used for elimination of redundant features. In the next step, PQ events are discriminated by support vector machines (SVMs) as classifier core. Obtained results of the extensive tests prove the satisfactory performance of the proposed method in terms of speed and accuracy even in noisy conditions. Moreover, the start and end points of PQ events can be detected with high precision.     

Disturbance detection in grid-connected distributed generation system using wavelet and S-transform

In this paper, wavelet transform and S-transform based approach is proposed for islanding detection and disturbance due to load rejection in distributed generation (DG) based hybrid system. The two types of distributed generation technology: inverter based and rotating machine based, that consists of photovoltaic, fuel cell and wind systems are considered in hybrid system configuration. The negative sequence voltage signal is analyzed through wavelet transform and S-transform for islanding detection of these resources from the grid. The above two approaches are also used in study of voltage profile at point of common coupling (PCC) with a non-linear load connected. The study for different scenarios in operation of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation (STD) of S-transform contour and wavelet transform signal is also reported for both islanding detection and disturbance due to load rejection.     

基于小波包和S变换的电火花机床用电负荷信号处理

电火花机床工况下的用电负荷是电网中带有突变性质的电能形式之一,提取其突变特性是研究这类电能特性的重要问题。文章采用不同小波基函数、阀值对电火花用电负荷特性进行去噪处理,并提出一种基于S变换的脉冲电能质量突变信息识别方法。分析结果表明,该方法提取准确率高。     

基于S变换和规则基的复合电能质量扰动识别

提出了一种复合电能质量扰动识别方法。为避免复合电能质量扰动类型中单一扰动相互影响而造成的特征混叠或失效问题,采用FFT变换结合动态测度法提取6个特征和S变换提取5个特征,从基频、中频、高频、基频标准差、频谱极值点对称等各个方面刻画扰动信号的特征;然后构建基于规则基“IF—THEN”形式的分类器,提取的特征输入分类器后能自动识别电能质量扰动类型。仿真结果表明,在一定噪声条件下,所提出的分类方法能准确识别26种扰动类型,其中包含8种单一扰动类型以及18种双重扰动类型。     

基于广义S变换的吸收衰减补偿方法

探讨了利用广义S变换代替短时Fourier变换或连续小波变换，进行吸收衰减补偿的方法。对短时Fourier变换、连续小波变换、S变换和广义S变换进行了分析和比较，给出了基于广义S变换的吸收衰减补偿方法。该方法的实现步骤是：①用广义s变换对高信噪比的叠加地震信号逐道进行时频分析；②在每个时间点，根据地层吸收特点提取各个频率的能量吸收衰减因子；③用加权方法对每个时间所对应的各个频率的广义S变换系数进行补偿，使各个频率在不同时间的能量相同；④将所有时间各个频率加权补偿的结果重构回地震记录，实现对地层吸收的补偿。模拟结果表明，广义S变换时频分析方法能够提高信号时频分布的分辨率。对实际二维地震数据的试算结果表明，基于广义S变换的吸收衰减补偿方法能较好地对地层吸收进行补偿，提高地震资料的分辨率，改善地震资料的品质。     

综合工频与特征频率零序导纳角差的选线方法

针对配网线路中大量电缆线路的使用导致故障线路与健全线路零序电流差异小的问题,提出一种基于线路自身工频与特征频率上的零序导纳相角差的故障选线新方法。在分析线路特征频率的基础上,利用S变换提取直流与特征频段内整倍工频频率点幅值信息,通过比较两部分幅值大小判断故障合闸角范围。基于特征频段内频率点幅值最大原则确定故障系统零序电流的特征频率,并进一步分析线路工频与特征频率下零序导纳向量特性。最后提出利用故障线路、健全线路的工频与特征频率零序导纳角差相差180°构建判据。基于Matlab的仿真验证本选线方法具有良好的选线效果。