谐波检测方法-中英文

谐波检测方法

谐波检测是谐波问题中的一个重要分支，对抑制谐波有着重要的指导作用，对谐波的分析和测量是电力系统分析和控制中的一项重要工作，也是对继电保护、判断故障点和故障类型等工作的重要前提。准确、实时地检测出电网中瞬态变化的畸变电流、电压，是众多国内外学者致力研究的目标。常规的谐波测量方法主要有三种：模拟带通或带阻滤波器的谐波测量；基于傅里叶变换的谐波测量；基于瞬时无功功率的谐波测量。

一、模拟滤波器：

最早的谐波检测方法是采用模拟滤波器来实现的。输入信号经放大后送入一组带通滤波器，滤波器的中心频率f1、f2、…fn是固定的，为工频的整数倍，且f1〈f2…〈fn，然后送多路显示器中显示测量信号中所含谐波成分及其幅值。该方法的实现电路简单，造价低，输出阻抗低，品质因数易于控制。但是电路较难设计，误差大，对电网频率波动和电路元件参数很敏感，使得检测出的谐波中含有较多的基波分量，运行损耗大。

二、快速傅立叶变换：

随着计算机和微电子技术的发展，开始采用傅立叶分析的方法来检测谐波和无功电流,有离散傅立叶变换（DFT）和快速傅立叶变换（FFT）两种。这种方法根据采集到的一个电源周期的电流值进行计算，最终得出所需的谐波和无功电流，它可以精确地分析和检测整数次谐波，目前应用比较广泛，技术也相当成熟。但由于傅立叶变换要进行两次变换，计算量大，计算时间长，所以检测时间比较长。

三、瞬时无功功率理论：

1983年日本学者赤木泰文提出的瞬时无功功率理论，即ｐ－ｑ理论解决了谐波和无功功率的瞬时检测和不用储能元件就能实现抑制谐波和无功补偿等问题，从而使得电力有源滤波理论由实验室的理论研究走向工作应用。根据该理论，可以得到瞬时有功功率ｐ和瞬时无功功率ｑ，ｐ和ｑ中都含有直流分量和交流分量。由此可得被检测电流的基波分量，将基波分量与总电流相减即得相应的谐波电流。因为该方法忽略了零序分量，对于不对称系统，瞬时无功的平均分量不等于三相的平均无功。所以，该方法只适用于三相电压正弦、对称情况下的三相电路谐波和基波无功电流的检测。

四、一种基于正交三角级数神经网络的谐波检测方法。

根据电力系统中非正弦周期电流的分解形式，提出了一种基于正交三角级数神经网络的谐波检测方法。方法能同时检测出非正弦周期电流中的基波分量与各次谐波分量的幅值和相位以及有功电流和无功电流。通过仿真实例验证，该方法能够把整数次谐波进行有效分离，用相对较少的数据量达到了较高的检测精度。在电力系统中，由于非线性负载的广泛应用，向电网注入了大量的谐波电流，使供电系统中的元件损耗增大，给电力系统中的设备运行带来很大危害。为了防止谐波危害系统安全运行，就必须确切掌握电力系统中畸变波形含有谐波的实际情况，采取相应措施对其进行抑制或补偿。FFT法是当今应用得最多的谐波检测方法，但FFT法在实际应用中存在着频谱泄漏问题，使得算出的各次谐波精度不高。将神经网络方法应用于电力系统谐波研究处于起步阶段，在谐波源辨识、谐波预

测与测量以及电力系统负荷预测等方面取得了一些成果。根据非正弦周期电流的分解式，提出了一种基于正交三角级数神经网络的谐波检测方法，本质上是利用三层神经网络的函数逼近性能给出了一种信号分离(分解)的方法, 该方法能够同时检测出非正弦周期电流中的基波分量与各次谐波分量的幅值和相位以及有功电流和无功电流，具有较高的检测精度。所用神经网络结构简单，激活函数采用一组正交的三角函数，算法容易实现，网络收敛速度较快。

五、一种基于自适应神经元在线整定的谐波检测方法，用于电流信号处理技术领域。

步骤如下：将电压作为参考输入，负载电流作为原始输入，通过实时运算输出与负载电流基波有功分量幅值、相位均相等的信号，将此信号从负载电流中扣除后，得到谐波和无功电流分量的总和；同时为了提高计算的速度和精度，对学习速率和积分环节的进行在线整定：通过对谐波电流的采样值数据窗分析，判断谐波是否发生或突变，在检测的初始阶段或者电流突变情况下，学习速率先取较大值，再取较小值；通过权值理论计算式可计算出权值初始值的估计值，从而以优化的初始值重新进行积分。本发明明显提高了算法速度和精度，优化了算法实时计算性能。

六、基于线调频小波变换的电机故障信号谐波检测方法。

线调频小波变换统一了短时Fourier变换和小波变换的时频分析，并能根据信号的特点自适应生成新的时频窗口。本文首次将线调频小波变换引进电力系统的突变信号处理中。分析了其消噪和滤除干扰的原理；构造了线调频小波变换的算法。该算法不仅能解决消噪和滤除干扰的问题，还能解决关于滤除整数(偶数)次和分数次谐波，并通过对电力系统突变信号处理的实例说明该算法的突出优点。

近年来，短时Fourier变换和小波变换在电力系统故障诊断、检测、定位、识别以及信号消噪、重构等方面的应用也有很大的进展。短时Fourier变换是一种使用固定大小的时频分析窗口的Fourier变换，适用于分析具有固定不变带宽的突变信号；小波变换使用时间和频率轴可伸缩的长方形时频分析窗口，适用于分析具有固定比例带宽(恒Q，即滤波器品质因数不变)的突变信号。这些使得它们在电力系统中信号处理某些方面如干扰、偶次谐波和非整数次谐波滤波等的应用受到一定的限制。因此寻找具有近似等Q的时频窗口的时频分析工具是非常必要的，它除了时间平移，频率平移和时频拉伸外，还应考虑矩形窗口的斜方向的拉伸与旋转变化。

线调频小波变换满足上述要求，使用的时频分析窗口除了时移、频移、尺度变化以外，最主要的是包含了时频窗口在时频平面上的放置以及在倾斜方向上的尺度变化(拉伸)。由于使用各种长方形和各种平行四边形的时频窗口，所以线调频小波变换可以分析具有非固定不变带宽和非固定比例带宽(非恒Q)的突变信号。

信号的消噪、滤除干扰、压缩、恢复以及故障信号检测、诊断、识别、定位是电力系统信号处理的主要工作，其目的是尽可能地复原被噪声或干扰污染的信息源以及故障的特征和类型。严格地讲，干扰和噪声是两个不同的概念。干扰指周期的、有规律的误差信号(测量信号与真实信号的差)；而理论上不能预测的、必须用概率统计刻画划的误差信号定义为噪声。电力系统在采样信号时，现场存在大量噪声和干扰信号，严重影响了系统、设备监测的灵敏度和可靠性，因此消除干扰和滤掉噪声是电力设备监测的一个关键技术问题。

在电力系统中，快速傅立叶(FFT)阈值滤波法和最小均方误差(LMS)自适应滤波器是最常用的用来抑制干扰和消噪方法。但是，FFT阈值滤波不能消除平稳随机型干扰，而LMS自适应滤波器收敛性能受时延、收敛因子等参数的影响，滤波效果不稳定，甚至有时不收敛。基于小波变换的干扰滤波器研究不多，文［7］在干扰滤波方面作了尝试，它将干扰信号分为脉冲型干扰、连续周期型干扰和平稳随机型干扰。主要讨论连续周期型和平稳随机型干扰信号的抑制。仔细分析后，文中对平稳随机型干扰即白噪声进行了基于小波变换的分析处理，对有色噪声未涉及。对连续周期型干扰滤波论及不多，小波变换对这类干扰应该也不是有效的。文［10］论及到连续周期型干扰滤波问题，它提出了3次B样条小波对采样信号进行预处理的方法，可基本消除偶次谐波和1.5次以上非整数次谐波。

The method of The Harmonics examination

The Harmonics examination is Harmonics an important branch within problem. which has an important instruction function to repress Harmonics. Is a power system analysis and control a key job to the harmonic analysis and the diagraph. Is also to after electricity protection, judgment the breakdown order and break down a type etc. work of important premise. Now how to accurate, quickly examine the distortion current, voltage is the target that numerous domestic and international scholars concentrate on a research. The Harmonics of normal measures method mainly have three methods: The Harmonics of measure according to the analogous filter; The Harmonics of measure according to Fourier transform; The Harmonics of measure according to the instantaneous reactive power.

First、(The analogous filter) the earliest Harmonics examination's method.

The Input signal sends into a set of band pass filter after amplify. The center frequency of filter f1、f2、…fn is fixed. Which is for the integral of run frequency doubly. f1〈f2 …〈fn. Then send many manifestation in and value. This method carries out electric circuit in brief, building pr the road displays diagraph signal composition in the Harmonics contained ice low, output resistance is low.The article prime factor is easy to a control. But the circuit is more difficult design, the error margin is big, to motion and circuit component of the charged barbed wire net frequency parameter very sensitive, Make to there is more base frequency in the Harmonics of examining weight, circulate to exhaust greatly.

Second、FFT(Fast Fourier transform)

Along with the technical development of the calculator and the micro-electronics, Start to adopting the method that the Fourier analysis to examine Harmonics and reactive current. This kind of method according to collect of an electric current value of power supply period carry on a calculation. Finally get the Harmonics needed by reactive current. It can analyze by the square time with examination integral Harmonics. But because the Fourier transform carries on two transformation. The calculation has great capacity, computing time is long, So examination time also is longer.

Third、(Instantaneous reactive power)

The Japanese scholar puts forward in 1983 of the moment has Instantaneous reactive power theories. Then the ｐ－ｑ theories solves Harmonics and reactive power of the moment examine with need not keep ability the component can carry out to repress Harmonics and reactive power compensation etc. problem. The theories research which makes active filter the theories from the laboratory thus heads for a work application. According to this theories. We can get to have a instantaneous active power

ｐand instantaneous reactive powerｑ. All imply direct current weight and exchanges weight in ｐandｑ. From here the base frequency can be measured. The base frequency weight's reducing mutually with total current have to correspond of Harmonics current. Because of that method neglected zero preface weights. The method's is applicable to three mutually voltage and under symmetry circumstance of three mutually electric circuit Harmonics.

Fourth、（Method of Harmonics Measurement Based on Neural Network of Orthogonal Trigonometric Series.）

Based on the neural network of orthogonal trigonometric series,an approach for h armonics measurement in power systems is presented in this https://www.360docs.net/doc/cb3961745.html,ing this method, the fundamental component and harmonics can be detected simultaneously with less data quantity. The simulation results validate that harmonics can be separa ted from a signal with high accuracy by the method developed in the paper. In the electrical power system, as a result of the misalignment load's widespread application, has poured into the massive harmonic current to the electrical network, causes in power supply system's part to lose increases, brings the very big harm for electrical power system's in equipment movement. In order to prevent the overtone harm system safety movement, must grasp in the electrical power system the distortion profile to include the overtone actual situation accurately, takes the corresponding measures to carry on to it suppresses or the compensation. The FFT law applies most overtone examination method now, but the FFT law has the frequency spectrum divulging problem in the practical application, causes various subharmonics precision which figures out not to be high. Applies the neural network method in the electrical power system overtone research is at the start stage, in the overtone source identification, the overtone forecast and the survey as well as aspects and so on electrical power system load forecast has made some progresses. According to the non-sinusoidal periodic current's decomposition type, proposed one kind based on the orthogonal trigonometric series neural network's overtone examination method, essentially uses three neural networks the approximation of function performance has given one kind of signal separation (decomposition) the method, this method can simultaneously examine in the non-sinusoidal periodic current the fundamental wave component with various subharmonics component peak-to-peak value and the phase as well as the wattful current and the idle current, has the high examination precision. Uses the neural network structure to be simple, the activation function uses group of orthogonals easily the trigonometric function, the algorithm to realize, the network convergence rate is quick.

Fifth （An adaptive neuron-tuning the harmonic detection measurement.）