A quadratic polynomial signal model and fuzzy adaptive filter for frequency and parameter estimation of nonstationary power signals

Accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in a wide variety of power system applications, like protection, control and state monitoring. With this objective, the paper presents a novel hybrid approach for the accurate estimation of dynamic power system frequency, phasor and in addition to suppressing the effect of harmonics/interharmonics and noise in the voltage and current signals. The algorithm assumes that the current during a fault occurring on a power system consists of a decaying dc component, and time variant fundamental and harmonic phasors. For accurate estimation of fundamental frequency, phasor, decaying dc and ac components in the fault current or voltage signal, the algorithm uses a quadratic polynomial signal model and a fuzzy adaptive ADALINE filter with a modified Gauss–Newton algorithm. Extensive study has been carried out to demonstrate the performance analysis and fast convergence characteristic of the proposed algorithm. The proposed method can also be implemented for accurate estimation of dynamic variations in the amplitude and phase angles of the harmonics and inter harmonics mixed with high noise conditions.     

A robust parametric method for power harmonic estimation based on M-Estimators

Power harmonic estimation is essential for evolving suitable protection and control strategies of power networks. Parametric approaches exhibit super-resolution and have been gradually applied in practice. However, they are susceptible to the presence of noise in the signals. The analysis accuracy will be abruptly reduced and even ineffective under non-Gaussian or impulsive noise environment. In this paper, a novel approach based on M-Estimators for harmonic estimation is proposed to overcome the above shortcomings. The derivation of Gauss–Newton iteration equations of harmonic frequency, amplitude and phase is presented. Further, the ESPRIT algorithm is also employed to acquire the initial values of harmonic frequencies, which avoids the objective function of M-Estimator to be stuck in local minima and improves the convergence rate of optimization. The numerical simulations and experimental results verify the feasibility and effectiveness of the proposed method.     

Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition

Harmonic decomposition makes a great impact on power system operation, especially devices with frequency converters are widely used in modern society. In this paper, a hybrid method based on improved empirical mode decomposition enhanced with masking signals is presented to extract single-frequency harmonics from disturbed power signals accurately. The parameters for building masking signals are optimized by cooperative chaotic particle swarm optimization, where the Logistic chaos and cooperative evolution are employed to improve the convergence accuracy and avoid trapping into local minima. For improving the performance further, the improved fast Fourier transform based on Nuttall window and harmonics pre-extracting procedure are introduced to enhance the decomposition accuracy and reduce the instantaneous magnitude error in extracting time-varying power signal. The synthetic and field experiments demonstrate that the proposed method reveals significant improvements in the integrality and decomposition accuracy of harmonics extracted from time-varying power signal.     

Method, algorithm and device for estimation of components above the harmonic frequency range up to 9 kHz

The paper presents a method of estimation of frequency groups with 200 Hz bandwidth in the frequency range from the 50th harmonic up to 9 kHz. The method consists of the application of a fast Fourier transform (FFT) for wavelet coefficients after input signal decomposition and partial synthesis for chosen frequency bands. It enables the computational complexity of the algorithm to be reduced and also attenuates influence of the fundamental component and low-frequency harmonics, as required by IEC Standard 61000-4-7. The particulars of this method are shown and analysis for a chosen wavelet family is provided. Further, the algorithm and its implementation in real device for power quality monitoring is presented. Finally, the results of measurements of two testing signals are shown. The required attenuation of fundamental component and required accuracy was obtained.     

ON-LINE PHYSICAL PARAMETER ESTIMATION WITH ADAPTIVE FORGETTING FACTORS

An on-line (or recursive) parameter estimation scheme based on a physical model is presented. Tracking of time-varying parameters is achieved by the use of a forgetting factor in the standard formulation. A new scheme for adapting the forgetting factor is proposed and validated on time-data from both simulated and experimental systems.     

小波神经网络用于光纤陀螺漂移误差辨识

提出了采用小波消噪和小波神经网络两个模型对光纤陀螺漂移误差进行辨识。应用小波分析方法消除高频噪声,改善信噪比,把消噪信号作为神经网络期望输出,然后采用带遗忘因子的递推最小二乘 (DRLS) 算法训练网络并调整权值。该算法不进行任何矩阵运算,在保持收敛速度快和精度高的前提下,极大地减少了计算量,提高了小波神经网络的实时性能,仿真结果表明辨识误差在1.5%以内。     

A hybrid unscented filtering and particle swarm optimization technique for harmonic analysis of nonstationary signals

This paper presents a modified unscented Kalman filter for accurate estimation of frequency and harmonic components of a time-varying signal embedded in noise with low signal-to-noise ratio. Further, the model and measurement error covariances along with the unscented Kalman filter parameters are selected using a modified particle swarm optimization algorithm. To circumvent the problem of premature convergence and local minima, a dynamically varying inertia weight based on the variance of the population fitness is used. This results in a better local and global searching ability of the particles, which improves the convergence of the velocity and better accuracy of the unscented Kalman filter parameters. Various simulation results for nonstationary sinusoidal signals with time varying amplitude, phase and harmonic content corrupted with noise, reveal significant improvement in noise rejection and speed of convergence and accuracy in comparison to the well known extended Kalman filter.     

基于RLS算法的有源滤波器自适应基波检测方法

揭示了有源电力滤波器中谐波补偿指令相位的微小变化都将对谐波控制效果产生很大的负面影响，并导致新的谐波产生。而谐波补偿指令相位偏差主要是由谐波检测算法产生。针对电力系统中信号波形的局部周期性，提出和研究了基于递推最小二乘算法（RLS）的自适应谐波能量最小化基波检测算法，给出了均方意义下的收敛性分析结果。研究表明，在电力系统中出现过渡带及基波信号发生时变时，采用FFT算法的估计结果存在较大的相位偏移。RLS谐波检测方案较Kalman滤波器尤其是FFT方法计算量小，适时跟踪性能好，是有源滤波器中补偿指令检测的有效方法。     

基于TVAR的自适应时频分析及在故障诊断中的应用

研究了非平稳信号的时变自回归（TVAR）建模方法，通过引入基函数将非平稳时变参数的辨识转化为线性时不变问题的辨识；在此基础上，应用带遗忘因子的递归最小二乘算法进行参数估计，实现了信号的自适应时频分析。通过仿真算例将该法与短时Fourier变换、Wigner分布的结果相比较，验证了该方法时频分辨率高的优越性。最后，将该方法应用于轴承的故障诊断，结果表明，该方法用于故障诊断的特征提取是有效的。     

基于改进ICA-OMP优化原子分解的谐波和间谐波检测方法

针对原子分解中匹配追踪类算法存在的问题,提出一种结合帝国竞争算法(ICA)和正交匹配追踪算法(OMP)优化原子分解的电网谐波和间谐波信号检测方法。首先根据谐波和间谐波信号的特征,将Gabor原子库简化为正弦原子库。然后采用OMP算法对谐波和间谐波信号进行原子分解,通过设置合理的相关性阈值确定终止迭代次数。最后,根据搜寻出的最佳匹配原子的索引参数实现谐波和间谐波信号参数估计。在OMP算法迭代过程中引入ICA,可实现在连续参数空间中搜索最佳匹配原子,避免索引参数步长对检测精度的限制。算例仿真与实测表明本文提出的算法能够在噪声干扰情况下准确检测出各次谐波和间谐波分量,频率、幅值和相位的最大检测误差分别为0.015 4%、0.722 4%和1.512 6°,可有效分辨出频率相近的间谐波分量,实现时变谐波和间谐波分量的精确定位。与正交匹配追踪算法相比,计算复杂度缩减率在99%以上。     

Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct

This paper presents a new adaptive algorithm for active noise control (ANC) that can be effectively applicable to a short acoustic duct, such as the intake system of an automobile engine, where the stability and fast convergence of the ANC system is particularly important. The new algorithm, called the modified-filtered-u LMS algorithm (MFU-LMS), is developed based on the recursive filtered-u LMS algorithm (FU-LMS) incorporating the simple hyper-stable adaptive recursive filter (SHARF) to ensure the control stability and the variable step size to enhance the convergence rate. The MFU-LMS algorithm is implemented by purely experimental ways, and is applied to active control of noise in a short acoustic duct, and is validated using two experimental cases of which the primary noise sources are a sinusoidal signal embedded in white noise and a chirp signal. The experimental results demonstrate that the proposed MFU-LMS algorithm gives a considerably better performance than other conventional algorithms, such as the filtered-x LMS (FX-LMS) and the FU-LMS algorithms.     

小波基TVAR建模时频分析及在故障诊断中的应用

研究了非平稳信号的时变自回归建模方法,提出了应用小波基函数将非平稳时变参数的辨识转化为线性时不变问题的辨识,在此基础上,应用带遗忘因子的递归最小二乘算法进行参数估计,实现了信号的自适应时频分析。通过仿真算例将该法与短时傅里叶变换、Wigner分布的结果相比较,验证了该方法时频分辨率高的优越性。最后,将该方法应用于轴承的故障诊断,结果表明,该方法用于故障诊断的特征提取是有效的。     

基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法

复杂环境下的量测粗差和时变噪声严重影响了状态估计的精度和可靠性,对此提出了一种基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法。首先,基于先验和后验两阶段更新将变分贝叶斯推断引入因子图优化框架中,以估计时变量测噪声协方差;其次,利用相邻帧间的平均新息构造量测协方差预测值,作为粗差判据来实现稳健估计。基于INS/GNSS组合导航的仿真和现场实验评估表明,所提方法能在粗差干扰的情况下有效估计时变量测噪声,相比M估计和滑动窗口自适应因子图优化算法的水平定位误差分别减小了26.7%和39.8%,兼顾了估计精度和抗差性能,具有较好的复杂环境适应性。     

Fast algorithms for frequency, amplitude and phase evaluation of nonsinusoidal signals with noises

A fast and accurate algorithm for frequency, amplitude and phase estimation of the signals with white Gaussian noises is proposed in this paper. The proposed algorithm need two sample and computation process, one of which is used for frequency estimation in half cycle of the signal and another of which is used for amplitude and phase estimation in another half cycle. The proposed algorithm spends at most 1 cycle. Frequency estimation is based on numerical differentiation, and amplitude and phase estimation is based on fast Fourier Transform. With an initial sample frequency of 512 × 50 Hz, the signal is sampled and the frequency of the signal with white Gaussian noises is estimated at an error of 0.001% over a range of 1 Hz–1000 kHz. With another sample frequency based on the estimated frequency, the signal is once again sampled and the amplitude of the signal is estimated an error of 0.001% over a range of 1 V–320 V and the phase angle of the signal is estimated an accuracy of 0.001% over a range of 0–360. Using Matlab software, the simulation results of the test example are satisfactory.     

基于相关自适应器的EM-MWD信号检测系统设计

经EM-MWD(electromagnetic method measurement while drilling)电磁通道传输至地表的信号很微弱并且极易受到白噪声、奇异噪声、工频噪声及其谐波等干扰,导致信号特征参数提取的准确度降低,为了解决这一难题,通过对电磁波传输信道的研究,根据接收初始信号强度以及自适应检测和相关检测的特点,研究并设计了相关自适应器,并基于此设计了电磁随钻地表信号检测系统。然后用Hilbert变换求信号包络,完成了数据拟合和残差分析,并计算了信噪比、均方根误差和误码率,最后做了实验。仿真和实验结果表明,利用该检测系统,能够提高信号特征参数的准确度,达到有效降噪目的,对后续分析和研究提供了保证。     

基于变分贝叶斯的自适应 PF-SLAM 方法研究

针对移动机器人即时定位与地图构建中时变观测噪声及粒子位置分布对SLAM精度的影响,本文提出基于变分贝叶斯的自适应PF-SLAM算法,采用高斯混合模型对时变的观测噪声建模,并通过变分贝叶斯方法,迭代估算出混合模型中的未知参数;同时根据粒子权值将粒子划分为固定粒子和优化粒子,通过粒子间的近邻拓扑位置关系调整粒子分布,处理时变观测噪声与优化粒子的位置分布,使得优化的粒子集可以更好地表示机器人位置概率分布,实现观测噪声及粒子位置分布自适应。仿真实验表明本算法对比传统PF-SLAM算法定位与地图构建误差降低了76.45%。实际实验表明本算法处理下的环境轮廓误差对比传统PF-SLAM算法的环境轮廓误差减小了61.87%。该算法有效提高了移动机器人的状态估计精度,为移动机器人即时定位与地图构建提供了新的参考。     

基于置信度传播的变分自适应协同导航方法

协同导航过程中先验信息的准确性是保证协同导航系统精度和可靠性的重要关键因素。针对协同导航系统在复杂环境下会因外界干扰产生未知且时变噪声问题,提出一种基于置信度传播的变分自适应协同导航方法(SWSP)。首先以置信度传播(SPBP)协同导航贝叶斯框架为基础,完成基于置信传播机制的前向滤波;随后通过IW处理过程噪声和量测噪声作为贝叶斯估计的先验信息;进而利用前向滤波值构造滑动窗口对噪声进行平滑估计,从而解决因噪声时变而造成的协同导航系统滤波精度下降问题。仿真结果表明:当噪声时变时,进行平滑操作的SWSP算法与未进行平滑操作的SPBP算法相比,位置误差降低了90%,精度更接近于最优opt SPBP算法。     

Response-only modal identification using random decrement algorithm with time-varying threshold level

Modal Identification from response data only is studied for structural systems under nonstationary ambient vibration. The topic of this paper is the estimation of modal parameters from nonstationary ambient vibration data by applying the random decrement algorithm with time-varying threshold level. In the conventional random decrement algorithm, the threshold level for evaluating randomdec signatures is defined as the standard deviation value of response data of the reference channel. The distortion of randomdec signatures may be, however, induced by the error involved in noise from the original response data in practice. To improve the accuracy of identification, a modification of the sampling procedure in random decrement algorithm is proposed for modal-parameter identification from the nonstationary ambient response data. The time-varying threshold level is presented for the acquisition of available sample time history to perform averaging analysis, and defined as the temporal root-mean-square function of structural response, which can appropriately describe a wide variety of nonstationary behaviors in reality, such as the time-varying amplitude (variance) of a nonstationary process in a seismic record. Numerical simulations confirm the validity and robustness of the proposed modal-identification method from nonstationary ambient response data under noisy conditions.     

A new computing approach for power signal modeling using fractional adaptive algorithms

Estimating the harmonic parameters is fundamental requirement for signal modelling in a power supply system. In this study, exploration and exploitation in fractional adaptive signal processing (FrASP) is carried out for identification of parameters in power signals. We design FrASP algorithms based on recently introduced variants of generalized least mean square (LMS) adaptive strategies for parameter estimation of the model. The performance of the proposed fractional adaptive schemes is evaluated for number of scenarios based on step size and noise variations. Results of the simulated system for sufficient large number of independent runs validated the reliability and effectiveness of the given methods through different performance measures in terms of mean square error, variance account for, and Nash Sutcliffe efficiency.     

基于匹配追踪稀疏分解的电能质量扰动检测

根据基于冗余字典的匹配追踪(MP)信号分解思想,提出一种电能质量扰动信号的参数检测与特征波形提取方法。在MP算法的每次迭代中,首先采用快速傅里叶变换(FFT)搜索能量最大的频率成分,然后采用基于离散Gabor原子中心区域的简化内积计算方法获得扰动参数的估计值,并以该估计值作为初始解,采用BFGS算法做局部搜索,进而获得精确匹配参数,并结合基于递归的内积计算确定扰动的起止时刻,最后根据电力信号扰动波形特点,设计合成字典,确定与扰动成分最匹配的波形。对单一和混合电能质量扰动信号的分解实验表明,该方法可以实现扰动参数的快速精确检测,进而有效提取扰动特征波形,并具有较好的抗噪性能。