一种电压闪变实时检测的新方法

提出了一种基于数学形态学均值滤波与能量算子包络检测的电压闪变实时检测的新方法.区别于其它去噪滤波算法,改进的数学形态学的滤波器只含有加减和一次除法运算,而能量算子包络检测只需要对被测波形的三个样本进行两次乘法和一次减法运算,使得所提出的算法快速、简洁.仿真结果表明,所提算法能够准确地检测出闪变波形的包络,并且克服了Teager能量算子算法对噪声和突变的敏感性,适合电压闪变的实时检测.     

一种电压闪变实时检测的新方法

提出了一种基于数学形态学均值滤波与能量算子包络检测的电压闪变实时检测的新方法。区别于其它去噪滤波算法,改进的数学形态学的滤波器只含有加减和一次除法运算,而能量算子包络检测只需要对被测波形的三个样本进行两次乘法和一次减法运算,使得所提出的算法快速、简洁。仿真结果表明,所提算法能够准确地检测出闪变波形的包络,并且克服了Teager能量算子算法对噪声和突变的敏感性,适合电压闪变的实时检测。     

基于EEMD和Teager能量算子的电能质量扰动检测

针对电能质量扰动检测的问题,结合经验模态分解（EMD）理论和总体平均经验模态分解（EEMD）算法以及Teager能量算子（TEO）,提出基于EEMD和Teager能量算子的电能质量扰动检测方法。利用经验模态分解方法,将电力系统监测信号分解成不同特征时间尺度的单分量固有模态函数（IMF）,用Teager能量算子计算各固有模态函数的瞬时幅值和频率,得到扰动信号的幅值谱。该方法充分利用了EEMD的自适应性与Teager算子的快速响应能力,仿真试验结果验证了该方法的有效性。     

基于LMD和TEO的电能质量扰动检测分析

分析了局部均值分解LMD(local mean decomposition)在扰动检测中时间定位不足的原因,提出了基于LMD和Teager能量算子TEO(Tteager energy operator)的电能质量扰动信号检测分析方法。该方法由LMD和Teager能量算子2部分组成。首先利用LMD将电压信号分解成若干个乘积函数PF(product function),再用Teager能量算子解调PF分量得到信号的瞬时幅值包络和瞬时频率。根据时频图频率突变点,可以有效地检测扰动发生的起止时刻。与LMD相比,所提出的方法具有频率、幅值检测准确,定位能力强,端部失真小等优点,能有效检测分析非平稳电能质量扰动信号。     

基于形态滤波和动态测度的电能质量扰动检测

针对电能质量扰动信号的检测与定位,详细分析了动态(Dyn)测度的特点并在其对噪声较为敏感的基础上,构造一种双结构数学形态滤波器并结合Dyn测度算法实现电能质量扰动检测。对电网中的扰动波形进行预处理,以滤除信号中的随机噪声。对去噪后的信号波形,运用Dyn测度算法,通过提取信号的极值点获取信息从而快速识别信号的畸变点,对扰动起止时间进行检测。MATLAB仿真结果表明所提检测方法具有运算简单、运行速度快、检测准确的优点。     

基于双端行波原理的多端输电线路故障定位新方法

针对已有多端输电线路故障定位方法误差大的缺陷,将双端行波法应用于多端输电线路的故障定位,提出一种基于快速本征模态分解(fast intrinsic mode decomposition,FIMD)和Teager能量算子(Teager energy operator,TEO)的多端输电线路行波故障定位新方法.首先对各测量点的电流线模分量进行FIMD分解,然后利用TEO计算分解所得IMF1分量的瞬时能量,根据首个能量突变点来确定故障初始行波的到达时刻.其次,利用双端行波定位原理和初始行波到达时刻形成文章所提故障支路判定矩阵,利用该矩阵元素特征判定出故障支路.最后,选择能够经过故障点形成双端支路最多的母线作为初始端,计算自初始端经故障点到其他各节点的双端支路的故障距离,将这几个故障距离的均值作为最终故障距离.结果表明,利用所提方法能够准确快速地检测出故障初始行波的到达时刻和确定出故障支路,并进而准确定位出故障点.     

基于预测机制的电能质量扰动检测方法研究

通过对现有各种扰动监测算法的研究和比较,提出了一种基于线性预测机制的时间域电能质量扰动检测方法.将待分析的电网信号通过由线性预测机制得到的误差预测滤波器,当电网中发生电能质量扰动时,预测信号波形会在扰动发生及结束时刻产生突变,使得所提检测算法能够及时检测到电网中发生扰动信号的起始时刻和持续时间.仿真和试验结果表明了所提方法能准确检测电压暂升、暂降等变化相对缓慢且延续时间也相对较长的暂态扰动信号,对电压骤降等变化时间极为短暂的瞬时性扰动信号依然具有良好的监测效果.     

奇异值分解和Teager能量算子的电压暂降方法

针对噪声干扰严重影响电压暂降检测分析精度的问题,提出一种采用奇异值分解和Teager能量算子（Teager energy operator,TEO）进行电压暂降检测的改进算法,即采用 Hankel矩阵对电压暂降信号进行奇异值分解法（singular value decomposition,SVD）,从而获得降噪后的供电点上电压暂降近似信号;利用Teager能量算子跟踪该近似信号的瞬时幅值;最后通过仿真算例验证该方法的可行性和有效性。     

结合小波变换和能量算子的电压暂降检测方法

针对电压暂降扰动的准确定位与精确测量问题,提出结合小波变换与能量算子的暂降检测新方法.该方法通过小波变换将被检测电压暂降扰动分解成近似信号和细节信号两部分.在细节信号部分精确定位扰动发生起止时刻;对近似信号运行能量算子,能快速准确测量到暂降幅值.所提方法中小波变换兼具滤波效果,减弱或去掉低频暂降信号中的高频扰动成分,从...     

基于EMD和TEO的电力系统短期扰动检测新方法

借鉴经验模态分解EMD(Empirical Mode Decomposition)的思想提出一种电力系统短期扰动检测定位的新方法,该方法通过一个对称三角模态来保留信号的扰动信息进而定位扰动.同时,在扰动准确定位的基础上,提出对原信号进行分段EMD,并采用Teager能量算子求取幅值包络来识别扰动类型的分析方法.该方法能够克服EMD过程产生的模态混叠导致检测失效的问题,与Hilbert-Huang 变换方法和小波检测方法相比,所提方法能够更加简单快速地实现对扰动的检测.仿真结果表明了该方法的有效性.     

On power quality indices and real time measurement

Power quality (PQ) indices are used to quantify the quality of the power supply and serve as the basis for comparing the negative impacts of different disturbances on power networks. To overcome the limitations and deficiencies of the practical applications of some power quality indices in common use, a set of three new indices, namely the fundamental frequency deviation ratio (FDR), waveform distortion ratio (WDR), and symmetrical components deviation ratio (SDR) are proposed in this paper to summarize different types of power disturbances in a comprehensive manner. As instantaneous quantities, these novel indices can reveal the time varying characteristics of power disturbances in real time. Hence, the new PQ indices can well accommodate practical waveform distortions in power networks, which may be caused by multiple types of time varying power disturbances. They can therefore be further used to evaluate both the effectiveness and dynamic responses of PQ mitigation equipment in practical applications. To fully realize the advantages of the new PQ indices, a novel Atom (transform kernel) based time frequency transform and its recursive algorithm are also proposed as the supporting measurement technique. The new Atom approach can continuously measure the instantaneous frequencies and amplitudes of signal components in a nonstationary disturbance waveform with high accuracy, and then update the new PQ indices at each sample. The effectiveness of the new PQ indices and the supporting measurement technique were ascertained using various PQ events, both simulated events and those recorded at an industrial site.     

Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm

In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQ combined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions.     

移相电能质量检测法的研究

电能质量检测是电能质量研究的一个重要组成部分,该文提出了一种移相电能质量检测方法。该检测方法原理简单、实时性好,可对电力系统最重要的几种电能质量扰动,包括谐波、电压凹陷、电压凸起、电压波动和暂态振荡,进行检测,且方法本身没有延时,可应用于电能质量实时检测和识别系统中。该文运用大量的仿真数据和某牵引变母线电压现场采样数据对检测有效性进行了验证。     

电能质量扰动信号的自适应去噪方法

有效地降低电能质量信号中的噪声,是做好电能质量信号检测、识别等工作的基础。为了克服一维电能质量信号降噪的难点问题,即有效地去除噪声并完整地保留奇异点的特征,对目前图像处理领域中针对高斯等噪声降噪性能最好的基于块匹配的三维变换域联合滤波(BM3D)算法进行了改进,提出一种电能质量扰动信号的自适应去噪新方法。该方法参数较少,无需估计噪声方差,也无需人为设定滤波阈值,而是通过自适应估算较为准确的阈值实现离散余弦变换(DCT)域的滤波。通过对电压中断、电压暂降、电压暂升、脉冲暂态、振荡暂态和谐波这6种常见的电能质量信号进行降噪仿真实验,并与应用较为广泛的小波阈值去噪法进行对比分析,最后应用于实际电能质量扰动数据的降噪,验证了所述算法的有效性。     

A real-time classification method of power quality disturbances

This paper describes a real-time classification method of power quality (PQ) disturbances. With an acceptable computation burden, both the elementary parameters of the power signal and the types of the disturbances in the power signal are obtained easily. The proposed method addresses the selection of discriminative features for detection and classification of PQ disturbances. Five distinguished time-frequency statistical features of PQ disturbances are extracted using RMS (root-mean-square) method and discrete Fourier transform (DFT). Using a rule-based decision tree (RBDT), the nine types of PQ disturbances can be recognized easily and there is no need to use other complicated classifiers. Finally, the proposed method is tested using the simulated waveforms. And some preliminary experimental results of the accuracy characterization of an initial development instrument are reported. The simulation and application results validate the accuracy and efficiency of the proposed method.     

基于S变换和多级SVM的电能质量扰动检测识别

提出了一种基于S变换和多级支持向量机(SVMs)的电能质量扰动检测和识别方法.首先通过S变换对电能质量扰动信号进行时频分析,有效实现对各种扰动的检测输出.然后对检测输出进行时频特征提取,并通过一个N?1级支持向量机器分类器,最后实现N种电能质量扰动信号的分类识别.测试结果表明,该方法能有效识别参数大范围内随机变化的各种电能质量扰动,识别正确率高,且训练时间很短,实时性能好.     

Classification of power quality disturbances using non-linear dimension reduction

The proposed technique is different from others in respect that it is based on the concept of local non-linear relation and uses non-linear fuzzy functions to extract the feature-specific data. To extract any change during change in the patterns of power quality (PQ) events, non-linear Gaussian functions have been used which results in the formation of fuzzy lattices. The fuzzy lattices have been expressed in the form of Schrödinger equation to find the kinetic energy (KE) used corresponding to any change occurring in the power quality disturbances. Finally, the KE value embedded in two-dimension space has been used to distinguish PQ events. The method is applied to classify the various PQ events such as transient, sag, swell and harmonics and results are simulated using MATLAB version 7.3. The simulated results validate that the proposed algorithm can efficiently distinguish the PQ events in a single cycle and work perfectly in real time.     

Classification of disturbances in hybrid DG system using modular PNN and SVM

This paper presents the classification of islanding and power quality (PQ) disturbances in grid-connected distributed generation (DG) based hybrid power system. The penetration of DG influences the PQ levels in the distribution networks. Islanding disturbances are separated out from the PQ disturbances based on the selection of suitable threshold value, at the initial stage of classification process. Further, the power quality disturbances are automatically classified into distinct classes based on feature extraction using S-transform followed by training of two classifiers, namely, modular probabilistic neural network (MPNN) and support vector machines (SVMs). Five different types of disturbances are considered for the classification problem. The study reveals that S-transform (ST) in association with MPNN and SVM can effectively detect and classify islanding and PQ disturbances. The proposed methodology uses features instead of real data set and thereby reduces the data size to classify disturbance signal without losing its original property. The accuracy and reliability of proposed classifier is also tested on signals contaminated with noise and PQ disturbances caused due to wind speed variation on an experimental prototype set-up.     

电能质量市场理论的初步探讨

随着电能质量问题的日益突出和电力市场改革的日渐深化,电能质量问题的解决已转化为技术与经济交融的问题;同时,电能质量作为重要的电力市场辅助服务内容,必然要在市场环境下解决。文中首先阐述了电能质量市场的基本概念,进而明确了电能质量市场理论研究的内容和基本理论框架,论述了电能质量市场理论研究的方法,阐述了建立电能质量市场理论的重要理论基础,并提出了今后研究中需要解决的一些问题。     

A de-noising scheme for enhancing wavelet-based power qualitymonitoring system

By means of the wavelet transform (WT), a power quality (PQ) monitoring system could easily and correctly detect and localize the disturbances in the power systems. However, the signal under investigation is often corrupted by noises, especially the ones with overlapping high-frequency spectrum of the transient signals. The performance of the WT in detecting the disturbance would be greatly degraded, due to the difficulty of distinguishing the noises and the disturbances. To enhance the capability of the WT-based PQ monitoring system, this paper proposes a de-noising approach to detection of transient disturbances in a noisy environment. In the proposed de-noising approach, a threshold of eliminating the influences of noises is determined adaptively according to the background noises. The abilities of the WT in detecting and localizing the disturbances can hence be restored. To test the effectiveness of the developed de-noising scheme, employed were diverse data obtained from the EMTP/ATP programs for the main transient disturbances in the power systems as well as from actual field tests. Using the approach proposed in this paper, remarkable efficiency of monitoring the PQ problems and high tolerance to the noises are approved