基于FFT的非整数次谐波参数检测算法

电力系统存在大量非整次谐波,快速傅立叶算法直接用于电力系统非整次谐波分析存在较大误差。分析了误差较大的原因,给出了用于非整次谐波分析的分析窗宽度,在采样时间为10倍工频周期的基础上,提出了基于Hanning窗的非整次谐波的幅值,频次和相位的计算公式。仿真结果显示,新算法具有很高的计算精度。     

一种新型基频变窗音频信号分析/合成系统

音频信号短时谱的基频随时间会发生变化,因此其谐波成分之间的间隔也会发生变化,在时域上信号随时间会发生或快或慢的变化,这导致短时谱分析所要求的时域和频域分辨率随时间是变化的.传统的固定分析窗由于其时频分辨率固定,无法同时满足上述要求,因而对短时分析造成偏差.本文基于正弦加噪声模型提出了一个分析窗宽受基频控制的自适应新型音频信号分析/合成系统方案,有效地提高了对信号实时分析的精度.并在此基础上,进一步对分析窗的使用、正弦成分的确定和追踪以及噪声成分的分离提出了新的算法和理论依据.本系统对实现音频信号的人为改造提供一套灵活高效的系统框架基础.     

基于改进的SVD和Prony的谐波检测算法

针对传统的Prony算法在谐波及间谐波检测过程中对噪声敏感,导致辨识精度不高的问题,提出了一种基于奇异值分解(Singular Value Decomposition,SVD)和Prony的改进算法。基于SVD理论,提出了一种奇异点辅助算法,自适应地选取奇异值分解的有效阶次,从而精确地滤除噪声信号。基于已确定的有效阶次,利用改进的Prony算法对滤除噪声之后的信号进行参数辨识,可以准确地估计出各个谐波及间谐波分量的参数。通过MATLAB仿真分析,表明算法能够准确地提取出电力信号的参数信息,具有一定的应用价值。     

一种基于改进DFT算法的相位差测量研究

相位差是指两路同频率周期信号初相位的差,在工业系统中有广泛应用,传统DFT算法测量工频相位差存在很大的频谱泄露误差。论文提出一种改进DFT相位差算法,使用布莱克曼自卷积窗对信号截短,并利用离散频谱校正方法修正有用频谱处的相位,并计算出相位差。仿真实验证实,在存在多次谐波干扰条件下,改进DFT算法能够克服频率波动和白噪声干扰,算法精度比现有的矩形自卷积窗插值算法提高2个数量级以上,可以实现相位差的高精度测量。     

谐波法复杂周期信号周期测量

利用复杂周期信号频谱存在大量等间隔幅度起伏的谐波分量.提出了复杂周期信号的谐波法周期测量.谐波法之FT-Abs-IFT(FAIF)是周期信号频谱包络的反傅里叶变换,输出为频谱包络对应时域信号与周期冲击信号的卷积.其最大值是等幅度或起伏幅度(噪声条件下)的周期冲击信号,通过门限栓测和周期测量得到信号周期(或频率).最后通过与常用周期信号测量方法(同态滤波法、自相关法和AMDF)的仿真比较,表明对于复杂周期信号,谐波法之FAIF测量精度高,误差率低和良好的抗噪性能.     

电力系统间谐波和谐波分析的海宁窗插值算法

间谐波和谐波对电网的危害日益严重,所以准确计算出间谐波和谐波的幅值、频率和相位等参数,对于改善电能质量具有重要意义.在分析间谐波和谐波特性的基础上,提出了海宁窗插值的间谐波和谐波算法.该方法对时域信号加海宁窗进行离散傅里叶变换,初步求取各次谐波和间谐波的各个参数,然后对各参数采用插值算法进行修正.经过仿真验证,该方法可以减小频谱泄漏和同步偏差的影响,并为谐波和间谐波信号分析提供了有效、准确的分析结果.     

基于基波频率估计的多谐波分析

采用自适应陷波滤波器实现基波频率可变的多谐波(包含整数次谐波和非整数次间谐波)分析. 算法包括基波频率估计器和多个二维正弦跟踪器, 形成缓慢自适应积分流形, 用李雅普诺夫定理和平均方法证明积分流形的存在性和稳定性. 若滤波器频率系数和信号的谐波结构相同, 该自适应陷波滤波器是一致渐近稳定的, 可按指数收敛准确跟随基波频率、每个谐波(间谐波)及其幅值. 导出了频率特性表达式和频率特性矩阵, 分析了滤波器参数对稳态频率特性的影响. 通过仿真证实算法的有效性, 并说明减小滤波器带宽参数和自适应增益能够获得更好的噪声特性.     

基于EEMD-RobustICA和Prony算法的谐波和间谐波检测方法

针对经验模态分解存在的模态混叠现象和Prony算法对噪声敏感的问题,将总体经验模态分解与鲁棒性独立分析法和Prony算法进行有机的结合,应用到谐波和间谐波的检测中。首先将含有噪声的谐波信号进行总体经验模态分解,得到不同阶数的固有模态函数,然后将其作为鲁棒性独立分量分析法的输入,对得到的独立分量进行软阈值去噪后进行逆变换得到重构后的固有模态函数,叠加得到去噪后的信号,最后用Prony算法对谐波和间谐波信号进行参数辨识,得到谐波和间谐波的参数。仿真结果表明,该方法具有较好的抗噪性,克服了Prony算法对噪声敏感的缺点,有效地提高了谐波和间谐波检测的精度。     

基于SPTool加窗傅里叶变换的谐波信号分析

SPTool是一种交互式图形环境的滤波器设计与信号分析工具,本文利用其信号观察器和频谱分析模块,完成对谐波信号的分析处理。通过加不同窗函数的仿真分析表明,汉宁窗的傅里叶变换对于非整数次谐波的分析具有较好的效果,能够准确的区分各次谐波含量;但对于含有噪声的谐波信号却不能得出精确地频谱信息。     

基于加窗插值和ESPRIT的电力系统间谐波算法

临近谐波或基波的间谐波是导致电压闪变的直接原因.为准确检测间谐波,通过分析加窗插值算法和ESPRIT的局限性,提出基于加窗插值和ESPRIT的间谐波检测方法.通过加窗插值求解各个信号参数,采用频率分析定位临近谐波和间谐波所在频率区间,并采用ESPRIT算法计算时域滤波后的残差分量.仿真对比结果表明,该方法的频率分辨率和精度均优于加窗插值法和传统ES-PRIT算法,能有效消除伪谱的影响.     

Normalized estimation of fundamental frequency and measurement of harmonics/interharmonics

An adaptive notch filter is presented to estimate the fundamental frequency and measure both harmonics and interharmonics of an almost periodic signal with unknown time-variant fundamental frequency, which has the robustness that the convergence speed is determined by neither amplitude nor frequency of fundamental component. The algorithm forms a one-dimensional slow adaptive integral manifold whose existence and stability are proved by averaging method and Lyapunov stability theorem. The local exponential stability and the ultimate boundedness of fundamental frequency estimation are proved. The local exponential stability makes sure that the fundamental frequency, the harmonic and interharmonic components can be all fast tracked. The principle for adjusting the parameters with their influences on transient and steady-state performance is investigated and decreasing parameters can improve noise characteristic. The validity is verified by simulation results.     

小波变换在传感器信号滤波中的应用

在流速测量过程中采集的加速度传感器信号是含有随机干扰的信号,为了改善滤波效果,尽可能排除随机信号的干扰,本文介绍了小波变换理论在加速度信息滤波中的应用,并进行了仿真实验。仿真结果表明,小波变换具有良好的时频特性,能有效检测出信号中所含各频率成分,减小测量误差,在传感器信号滤波方面有广阔的应用前景。     

基于小波变换的间谐波检测与去噪

间谐波是一种特殊的谐波,它的大量存在对电能质量造成了污染,因而对其进行检测和分析具有十分重要的意义。文中分析了小波变换的特性及用于间谐波检测的优势,采用了正交小波多分辨率分析算法检测间谐波,并选择小波阈值法对信号进行去噪处理。仿真结果表明,小波变换具有良好的时频特性,能有效检测出信号中所含各频率成分,并且能有效的抑制和消除噪声。随着小波理论的进一步发展和完善,其在电能质量信号检测方面的应用将越来越广泛。     

The transformation-driven architecture for interactive systems

We propose new software architecture for interactive systems (systems with interacting components). This architecture, called the Transformation-Driven Architecture, TDA, uses certain ideas of the Model-Driven Architecture, MDA. However, unlike MDA, which uses models and model transformations at software development time, TDA uses them at runtime. To describe the dynamics of the system as well as interaction between the components, TDA uses special objects called events and commands.     

BFO optimized RLS algorithm for power system harmonics estimation

In this paper a new estimation approach combining both Recursive Least Square (RLS) and Bacterial Foraging Optimization (BFO) is developed for accurate estimation of harmonics in distorted power system signals. The proposed RLS–BFO hybrid technique has been employed for estimating the fundamental as well as harmonic components present in power system voltage/current waveforms. The basic foraging strategy is made adaptive by using RLS that sequentially updates the unknown parameters of the signal. Simulation and experimental studies are included justifying the improvement in performance of this new estimation algorithm.     

A backstepping high-order sliding mode voltage control strategy for an islanded microgrid with harmonic/interharmonic loads

This paper presents a new nonlinear voltage control strategy based on backstepping control and a high-order sliding mode differentiator for an islanded microgrid. The microgrid consists of multiple distributed generation (DG) units with an arbitrary configuration that can be parametrically uncertain or topologically unknown. The proposed controller robustly regulates the microgrid voltages in the presence of parametric uncertainties, unmodeled dynamics, load imbalances, and nonlinear loads with harmonic/interharmonic currents. In contrast to existing methods, the controller does not need to know the frequency of harmonic and interharmonic current of microgrid loads that lead to the reduction of the steady-state error of the voltage controller in the frequency of unknown harmonics and interharmonics. The MATLAB/SimPowerSystems toolbox has verified the proposed control strategy's performance.     

Improved Signal-to-Noise Ratio Estimation for Speech Enhancement

This paper addresses the problem of single-microphone speech enhancement in noisy environments. State-of-the-art short-time noise reduction techniques are most often expressed as a spectral gain depending on the signal-to-noise ratio (SNR). The well-known decision-directed (DD) approach drastically limits the level of musical noise, but the estimated a priori SNR is biased since it depends on the speech spectrum estimation in the previous frame. Therefore, the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. The consequence of this bias is an annoying reverberation effect. We propose a method called two-step noise reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. The estimation of the a priori SNR is refined by a second step to remove the bias of the DD approach, thus removing the reverberation effect. However, classic short-time noise reduction techniques, including TSNR, introduce harmonic distortion in enhanced speech because of the unreliability of estimators for small signal-to-noise ratios. This is mainly due to the difficult task of noise power spectrum density (PSD) estimation in single-microphone schemes. To overcome this problem, we propose a method called harmonic regeneration noise reduction (HRNR). A nonlinearity is used to regenerate the degraded harmonics of the distorted signal in an efficient way. The resulting artificial signal is produced in order to refine the a priori SNR used to compute a spectral gain able to preserve the speech harmonics. These methods are analyzed and objective and formal subjective test results between HRNR and TSNR techniques are provided. A significant improvement is brought by HRNR compared to TSNR thanks to the preservation of harmonics.     

iPEEH: Improving pitch estimation by enhancing harmonics

Pitch estimation is quite crucial to many applications. Although a number of estimation methods working in different domains have been put forward, there are still demands for improvement, especially for noisy speech. In this paper, we present iPEEH, a general technique to raise performance of pitch estimators by enhancing harmonics. By analysis and experiments, it is found that missing and submerged harmonics are the root causes for failures of many pitch detectors. Hence, we propose to enhance the harmonics in spectrum before implementing the pitch detection. One enhancement algorithm that mainly applies the square operation to regenerate harmonics is presented in detail, including the theoretical analysis and implementation. Four speech databases with 11 types of additive noise and 5 noise levels are utilized in assessment. We compare the performance of algorithms before and after using iPEEH. Experimental results indicate that the proposed iPEEH can effectively reduce the detection errors. In some cases, the error rate reductions are higher than 20%. In addition, the advantage of iPEEH is manifold since it is demonstrated in experiments that the iPEEH is effective for various noise types, noise levels, multiple basic frequency-based estimators, and two audio types. Through this work, we investigated the underlying reasons for pitch detection failures and presented a novel direction for pitch detection. Besides, this approach, a preprocessing step in essence, indicates the significance of preprocessing for any intelligent systems.     

Coupled person orientation estimation and appearance modeling using spherical harmonics

We present a novel approach for the estimation of a person's overall body orientation, 3D shape and texture, from overlapping cameras. A distinguishing aspect of our approach is the use of spherical harmonics for 3D shape- and texture-representation; it offers a compact, low-dimensional representation, which elegantly copes with rotation estimation. The estimation process alternates between the estimation of texture, orientation and shape. Texture is estimated by sampling image intensities with the predicted 3D shape (i.e. torso and head) and the predicted orientation, from the last time step. Orientation (i.e. rotation around torso major axis) is estimated by minimizing the difference between a learned texture model in a canonical orientation and the current texture estimate. The newly estimated orientation allows to update the 3D shape estimate, taking into account the new 3D shape measurement obtained by volume carving.     

Monaural Musical Sound Separation Based on Pitch and Common Amplitude Modulation

Monaural musical sound separation has been extensively studied recently. An important problem in separation of pitched musical sounds is the estimation of time-frequency regions where harmonics overlap. In this paper, we propose a sinusoidal modeling-based separation system that can effectively resolve overlapping harmonics. Our strategy is based on the observations that harmonics of the same source have correlated amplitude envelopes and that the change in phase of a harmonic is related to the instrument's pitch. We use these two observations in a least squares estimation framework for separation of overlapping harmonics. The system directly distributes mixture energy for harmonics that are unobstructed by other sources. Quantitative evaluation of the proposed system is shown when ground truth pitch information is available, when rough pitch estimates are provided in the form of a MIDI score, and finally, when a multi pitch tracking algorithm is used. We also introduce a technique to improve the accuracy of rough pitch estimates. Results show that the proposed system significantly outperforms related monaural musical sound separation systems.