一种模糊变步长自适应谐波检测算法

电力有源滤波器的成功应用依赖于精确的谐波电流检测技术。基于自适应干扰对消理论,提出一种基于模糊变步长推理的最小均方差（LMS）自适应谐波检测算法。通过分析影响LMS自适应谐波算法性能的不利因素,选取均方误差变化量和输入输出信号相关函数作为参量,建立模糊推理系统,自适应地调节算法的步长,实现谐波检测过程中,既能保证较快的动态响应速度和对噪声干扰的抑制,又能保持较高的检测精度,并通过计算机仿真及物理实验验证了该算法的有效性和可行性。     

Fast frequency and harmonic estimation in power systems using a new optimized adaptive filter

This paper presents an adaptive filter for fast estimation of frequency and harmonic components of a power system voltage or current signal corrupted by noise with low signal to noise ratio (SNR). Unlike the conventional linear combiner (Adaline) approach, the new algorithm is based on an objective function often used in independent component analysis for robust tracking under impulse noise conditions. However, the accuracy and speed of convergence of this algorithm depend on the choice of step size of the filter and its adaptation. Instead of choosing the step size η and the parameter β of the cost function by trial and error, an adaptive particle swarm optimization technique is used alternatively to obtain both η and β to reduce the error between the observed voltage or current samples and the estimated ones. Using the optimized values, the amplitude and phase of the fundamental and harmonic components are estimated. Further, the extracted fundamental component is used to estimate any frequency drift of the power system recursively using an optimized error function obtained from three consecutive voltage samples. To test the effectiveness of the algorithm, several time-varying power system signals are simulated with harmonics, interharmonics, and decaying dc components buried in noise with low signal-to-noise ratio (SNR) and are used to estimate the frequency and harmonic components. This approach will be useful in islanding detection of a distributed generating system.     

An adaptive linear combiner for on-line tracking of power systemharmonics

The paper presents a new approach for the estimation of harmonic components of a power system using a linear adaptive neuron called Adaline. The learning parameters in the proposed neural estimation algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corrupted with noise and decaying DC components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components of power system signals mixed with noise and decaying DC components     

自适应神经网络在谐波电流检测中的应用

为提高用于有源电力滤波器APF(active power filter)的谐波电流检测的性能,提出一种基于自适应神经网络的谐波电流检测方法.根据自适应噪声对消技术的基本原理,将基波电流从负载电流中滤除从而得到谐波电流.该方法能实时准确地检测出谐波,很好地弥补基于FFT方法、基于瞬时无功理论方法和基于小波变换方法等检测方法的缺陷.MATLAB/Simulink仿真结果证明该方法的实时性和准确性,可用于APF的谐波电流检测.     

基于离散频率自适应梳状滤波器的时变谐波分析

针对直流分量与多个谐波及间谐波分量叠加的时变电力信号,采用一阶低通滤波器与多个归一化频率估计器并联形成偏置频率自适应梳状滤波器,以快速准确估计直流分量以及每个谐波与间谐波分量的频率和幅值。算法的带宽参数与频率自适应增益分别对幅值与频率的收敛速度及估计精度具有不同的影响。利用经典四阶龙格-库塔方法获得相应的离散算法,并给出实现算法的程序结构。采用两个DSP系统组成实验系统,验证了算法的有效性。     

Symmetrical components estimation through nonrecursive Newton-type numerical algorithm

The paper reports new software developments for symmetrical components estimation. Nonrecursive Newton-type algorithm is extended with the second stage algorithm for symmetrical components calculation from the estimated fundamental phasors of three-phase signals (arbitrary voltages or currents). The algorithm is not sensitive to power system frequency changes and to the harmonic distortion of input signals. The algorithm is tested through computer simulations and by using laboratory obtained input signals and those recorded in the real distribution network.     

一种基于准均匀采样的电力谐波分析方法

针对电力谐波的准同步加窗分析法存在所用信号周期多、计算复杂和谐波泄漏分布不均匀等问题,基于准均匀采样提出了一种仅需1个信号周期特别适于单片机快速、准确实现的电力谐波分析方法。准均匀采样的时间离散误差不随连续采样而积累,在1个信号周期内取2的整数次幂个同步采样点,直接采用FFT算法即可实现谐波分析。基于信号的基波近似,并假设信号采样时的时间离散误差和幅值量化误差均服从均匀分布,对采用准均匀采样的电力谐波估计误差进行了分析。给出了基于准均匀采样电力谐波分析的算法和具体实现流程,流程中通过长整型变量对采样时间进行精确控制,算法简单高效。最后对准均匀采样谐波分析算法进行了仿真,结果表明基于通用单片机即可实现电力谐波的快速、准确分析。     

基于自适应SRUKF算法的电力系统动态谐波状态估计

针对传统无迹卡尔曼滤波(unscented kalman filter, UKF)谐波状态估计算法存在时变噪声和异常数据时估计准确度较差的情况,提出了一种基于自适应平方根无迹卡尔曼滤波(square-root UKF, SRUKF)的电力系统谐波状态估计算法。首先,针对时变噪声干扰,引入改进的Sage-Husa噪声估计方法实时估计噪声协方差。其次,针对异常数据干扰,引入异常数据修正方法,通过修正系数来降低异常数据对状态估计结果的影响。最后,通过搭建IEEE14节点系统验证自适应SRUKF算法的估计性能,能够有效地应用于电力系统的动态谐波状态估计。仿真结果表明,该算法在时变噪声和异常数据干扰时仍具有良好的估计性能。     

自适应频率跟踪的谐波电流检测方法

针对电网电压的频率在运行过程中会发生变化,导致谐波电流检测精度下降并直接影响到有源电力滤波器的补偿效果,为此提出了一种基于自适应频率跟踪的谐波电流分频检测方法。该法根据普罗尼谱估法中电导矩阵的频率变化情况,采用LMS算法在线优化从而能独立实时地检测出各相电流中的基波和各次谐波分量,并且直接处理检测结果,具有优良的跟随性能。仿真结果证明了该方法的可行性。     

Harmonic estimation in a power system using a novel hybrid Least Squares-Adaline algorithm

Nowadays many algorithms have been proposed for harmonic estimation in a power system. Most of them deal with this estimation as a totally nonlinear problem. Consequently, these methods either converge slowly, like GA algorithm [U. Qidwai, M. Bettayeb, GA based nonlinear harmonic estimation, IEEE Trans. Power Delivery (December) 1998], or need accurate parameter adjustment to track dynamic and abrupt changes of harmonics amplitudes, like adaptive Kalman filter (KF) [Steven Liu, An adaptive Kalman filter for dynamic estimation of harmonic signals, in: 8th International Conference On Harmonics and Quality of Power, ICHQP’98, Athens, Greece, October 14–16, 1998]. In this paper a novel hybrid approach, based on the decomposition of the problem into a linear and a nonlinear problem, is proposed. A linear estimator, i.e., Least Squares (LS), which is simple, fast and does not need any parameter tuning to follow harmonics amplitude changes, is used for amplitude estimation and an adaptive linear combiner called ‘Adaline’, which is very fast and very simple is used to estimate phases of harmonics. An improvement in convergence and processing time is achieved using this algorithm. Moreover, better performance in online tracking of dynamic and abrupt changes of signals is the result of applying this method.     

A New Power System Digital Harmonic Analyzer

In this paper, new digital instruments measuring power-quality indicators and harmonic analyzers are developed. A new technique for simultaneous local system frequency and amplitudes of the fundamental and higher harmonics estimation from either a voltage or current signal is presented. The structure consists of three decoupled modules: the first one for an adaptive filter of input signal, the second one for frequency estimation, and the third one for harmonic amplitude estimation. A very suitable algorithm for frequency and harmonic amplitude estimation is obtained. This technique provides accurate frequency estimates with error in the range of 0.002 Hz and amplitude estimates with error in the range of 0.03% for SNR = 60 dB in about 25 ms. The theoretical basis and practical implementation of the technique are described. To demonstrate the performance of the developed algorithm, computer simulated data records are processed. Data of the distribution power system voltage signals are also collected in the laboratory environment and are processed in a newly developed digital PC-based harmonic analyzer. It has been found that the proposed method really meets the need of offline applications. Even more, by using the parallel computation algorithms, this method should meet the need of online applications and should be more practical     

一种检测单相电路无功与谐波电流的跟踪算法

有源滤波器是改善电能质量的一种有效手段,它要求快速准确地检测出负载电流中的谐波和无功分量.针对单相系统瞬时谐波与无功检测方法算法复杂的问题,根据有功和无功功率定义,提出一种检测单相瞬时无功与谐波电流的快速跟踪算法.该方法通过在一个计算周期内,用低通滤波器和锁相环电路得到与电源电压基波同相频的标准正弦波与负载电流乘积后,采样存入采样数据存储队列,然后比较队列中基波周期前后一段数据来快速跟踪有功电流,从而得到实际补偿的谐波和无功电流.仿真表明该方法正确可行,在一个周期内可以实现对补偿电流的准确提取,而且对电流变化跟踪效果好,防干扰能力强.算法简单快速,只需两个乘法器,易于实现.     

基于神经网络的电力系统高精度频率谐波分析

加窗插值 FFT 算法是电力谐波分析常用的高精度算法，但在严重非同步采样情况下，其谐波分析精度有限。该文提出一种基于神经网络的高精度电力系统频率谐波分析算法。采样频率不能与实际基波频率同步时，该算法通过对与基波频率、谐波幅值及相位等相关参数进行更新，当神经网络收敛时，可以获得高精度的谐波分析结果。仿真结果表明，当基波频率在40~60Hz范围变化时，电力系统基波频率、基波和谐波幅值和相位的分析精度超过99.999 999 999%。     

Recursive functional expansion technique for computer-based impedance and differential protection

The development of a recursive functional expansion algorithm for extracting the desired frequency components from transient power system relaying signals is presented. The applications of this algorithm to impedance detection in transmission line protection and to harmonic restraint in transformer differential protection are discussed. The recursive algorithm generates fast fault detection timings for transmission lines and does not have restrictions on sample rate, data window or spacing of samples with respect to time. For power transformer differential protection, the combined second- and fifth-harmonic amplitude of the differential current is compared with the fundamental amplitude to arrive at a trip decision.     

并联有源电力滤波器的自适应重复控制

在现有电力有源滤波器重复控制方法的基础上,提出了自适应负载谐波电流数字检测方法和重复控制自适应信号发生器。基于瞬时无功功率理论,在d-q坐标系中实现谐波电流的数字检测算法,并引入自适应的每基波周期采样点数kmax消除电网电压频率变化的影响。采用比例控制与重复控制并联用于输出波形控制。实验结果证明了这种自适应的谐波电流检测和数字重复控制方式的有效性。     

氧化锌避雷器泄漏电流的全相位FFT分析

通过检测氧化锌避雷器泄漏电流,经过谐波分析提取泄漏电流基波和各次谐波参数,依据电流阻性分量可判断该装置在电网中的运行情况。为解决快速傅里叶变换(FFT)进行谐波分析时数据截断引起数字信号处理性能下降的问题,选用一种考虑包含某样点所有可能数据截断情况的全相位FFT分析方法,再基于此分析方法选用全相位时移相位差校正算法,同时利用窗谱函数推导出校正公式。仿真结果表明,该算法对比基于FFT的比值公式校正算法在无噪和有噪(50 dB)情况下谐波分析精度分别提高了4～5个数量级和1～2个数量级,且对相对小信号谐波分量的频率估计偏差不超过0.6 Hz,相位估计偏差不超过0.5°。该算法具有相位不变性和频谱泄漏抑制能力,实现了检测精度的提高和小信号参数的估计,并在泄漏电流检测系统中得到验证。     

基于自适应变步长最小均方的谐波检测算法的研究

从数字信号处理中的自适应噪声对消原理出发,介绍了一种改进的变步长最小均方(LMS)算法,该算法根据基波电流和谐波电流的不相关性,利用误差信号和参考输入的互相关估计来控制迭代步长,使得步长的更新不受谐波电流的影响。该算法原理简单,运算量小,易于实现,仿真结果表明了该谐波电流检测算法的有效性。     

多谐波源系统的非迭代式谐波潮流分析

为研究多谐波源系统的电能质量问题,提出一种快速的非迭代式谐波潮流分析算法。该算法将基频下的谐波源视为恒功率负荷,根据基频结果计算谐波源的运行参数及模型。联立求解系统的谐波导纳方程和谐波源模型方程,无需迭代即可得到系统中所有节点的各次谐波电压。该算法可考虑谐波源的各次谐波电压和谐波电流之间的相互耦合,也可考虑系统中多个谐波源之间的相互抵消作用。算法以AC/DC整流装置类谐波源为例阐述,可拓展应用于含其他非线性电力电子装置类谐波源的系统中。以存在多个分散式谐波源的实际系统为例,Matlab编程实现算法并与PSCAD时域仿真对比,结果表明:算法准确度高、计算速度快。     

Real-time electrical variables estimation based on recursive wavelet transform

In frequency and phasor estimation algorithms, the undesired components are required to be filtered out from the original signals. In power systems, the undesired components are the decaying dc offset and harmonics. These components could cause delay in algorithm convergence time and deviation from the desired results to a great extent. This paper proposes a new recursive algorithm for accurate and fast estimation of the instantaneous electrical variables such as frequency, amplitude and phase angle. The new algorithm provides an improvement over the existing recursive wavelet transform and, therefore, it is called IRWT. The IRWT performance is compared with the commonly used full-cycle discrete Fourier transform (DFT) and the recursive wavelet transform (RWT) methods. Since it uses a special mother wavelet function, it reduces computational complexity compared to the conventional DFT based method. Compared to the recursive wavelet transform (RWT) method, it has a faster response time. It is shown that IRWT possesses an improvement over a wide range of decaying dc component, harmonic distortions, frequency deviation and sampling frequency compared to the previously proposed methods. This characteristic of IRWT makes it a good candidate for the real-time applications in any power systems.     

Power system harmonic parameter estimation using Bilinear Recursive Least Square (BRLS) algorithm

The impact of nonlinear loads produces harmonic pollution in electrical power system. It is considered as a serious concern now a day. Whereas, many algorithms have been proposed for harmonic estimation to improve the power quality performance but till date the accurate estimation of power quality parameters remains a challenge. In this paper a non-linear adaptive algorithm, called Bilinear Recursive Least Square (BRLS), has been applied for the first time for estimating the amplitudes, phases and frequency in case of time varying power signals containing harmonics, sub harmonics, inter harmonics in presence of White Gaussian Noise. The technique is applied and tested for both stationary as well as dynamic signals containing harmonics. Practical validation of the proposed algorithm is also made along with the real time data obtained from a Variable Frequency Drive (VFD) panel used for controlling the speed and torque of the induction motor used at a large paper industry. Comparison of the results achieved with the proposed BRLS algorithm with two recently reported non-linear adaptive algorithms, Volterra Least Mean Square (VLMS), and Volterra Recursive Least Square (VRLS), reveals that the proposed BRLS algorithm is the best in terms of estimation accuracy and computational time.