基于高倍过采样与加窗插值FFT的电力谐波分析

为提高谐波分析精度,分析了信号加窗引起的信噪比损失以及AD转换产生的量化误差,阐述了过采样技术提高信噪比的原理。在此基础上,提出了基于高倍过采样和加窗插值快速傅里叶变换(fast Fourier transform, FFT)的谐波分析方法。该方法充分利用AD转换器的潜力,以尽量高的采样速率进行AD采样,同时通过均值滤波避免高倍过采样引起的采样数据量激增问题。详细研究了所提谐波分析方法对信号中谐波分量幅值和相位的影响,并给出了简洁实用的谐波幅值和相位校正方法。仿真表明,所提方法可在不增加系统成本的前提下改善加窗插值FFT的抗噪声能力,提高谐波分析精度。

基于FFT-LSTM的抽水蓄能发电机定子匝间短路故障诊断方法

定子短路故障是抽水蓄能发电机常见的故障之一,其会对发电机的性能和安全性产生严重影响,为确保抽水蓄能发电机安全稳定运行,提出基于FFT-LSTM的抽水蓄能发电机定子短路故障诊断方法。建立抽水蓄能发电机定子绕组匝间短路故障模型,分析定子绕组匝间短路故障时,发电机定子电、磁相关状态。以抽水蓄能发电机定子绕组匝间短路故障时的三相电流信号为依据,基于磁势相等原理将三相电流变换成两相电流后,利用FFT转换定子两相电流的时域信号为频域信号,获取故障电流频谱图输入LSTM网络中进行处理,输出抽水蓄能发电机定子绕组匝间短路故障诊断结果。实验结果表明,该方法可以更好地区分抽水蓄能发电机正常与故障状态,实现抽水蓄能发电机定子绕组匝间短路故障诊断,且故障诊断的交叉熵损失低。

基于FFT的波动方程VOFFLC控制

针对复杂波动方程的无穷维特性,基于Simulink平台利用FFT(Fast Fouri-er Transform)方法将其从时域PDE(Partial Differential Equations)模型转化为频域ODE(Ordinary Differential Equation)模型,并在频域上搭建类似于集中参数的控制系统。通过FFT和成熟FDM(Finite Difference Method)模拟实验结果的对比,证明采用FFT原理模拟PDE波动方程的思路正确;在频域ODE模型上施加自适应VOFFLC闭环控制,并设计了两种控制反馈规则。其中,采用乘法法则的VOFFLC控制时,波动呈现和原有形态一致、而周期缩短和振幅减小的现象;采用减法法则的VOFFLC控制时,可以实现类似边界控制的结果,然而在空间维度上可以实现向量级控制,即实现对该维度上任意函数形状、插值函数或者散点的向量级别控制,而这是边界控制做不到的。因而,基于FFT的波动方程VOFFLC控制有进一步的研究意义和广阔的实用价值。

基于分裂基FFT的S波段雷达信号处理单元设计与实现

回波信号的信息解调主要依赖于FFT完成,本文提出了一种基于分裂基FFT的FPGA实现信号处理单元方案,用于高效解调回波信号信息。在原方案中,距离维解调由DSP完成,速度维解调在PC端进行,但其采用的单核DSP无法同时处理距离维解调与数据上传,导致实时性较低。本文利用FPGA并行处理和灵活可编程的特点,将两次信号解调集成至FPGA中实现,并完成了数据处理与上传的并行设计,提高了数据处理的实时性与可靠性。其中分裂基FFT是实现上述功能的关键,本文通过设计自适应计算单元与数据流控制单元,提升了分裂基FFT的处理速度,相比传统结构,本文方案节省了5.4%以上的计算周期;相比改进后的分裂基FFT,可节省2.31%以上的计算周期。闭环测试结果表明,本文设计的集成化信号处理单元解调得到的目标点信噪比高达70 dB,能够很好地满足信号处理的要求。

接触问题的三角形载荷离散FFT加速算法

接触问题控制方程的有效求解,往往涉及到复杂的数学理论知识,而在实际工程应用中,接触应力分布又具有高度随机性。为高效快速求解任意载荷分布下固体的接触响应,基于三角形载荷离散单元,嵌入离散卷积快速傅里叶变换(DC-FFT)算法,提供了一种高精度、高可靠度的计算方法。相比于通常采用的分段均布载荷离散方法,三角形单元的解析求解略显复杂,但能更好地模拟接触载荷任意分布的特性,对于接触边缘处载荷由零递增或递减为零的情况,也可以予以充分考虑。为优化三角形载荷离散单元的求解方法,根据接触影响系数矩阵的“激励-响应”特性,推导了三角形载荷单元和均布载荷单元作用下的应力分量解析解。通过构造包含影响系数矩阵的离散卷积形式应力解,将某一目标节点在所有载荷单元作用下,重复度极高的矩阵运算叠加过程,采用DC-FFT算法来简化加速计算。通过程序编程计算,分析验证了所提出算法的精确度和高效性。

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

空间结构模型优化的弹性参数FFT-MA随机建模

快速傅里叶变换滑动平均(FFT-MA)法是一种灵活高效的随机建模方法,在地下介质高分辨率建模、复杂介质非平稳建模和不确定性评价等方面具有重要的应用价值。准确构建空间结构模型是利用FFT-MA方法生成合理的随机模型的关键。然而,以往对FFT-MA方法的研究并未提出准确构建空间结构模型的有效方法。为此,文中提出了一种有效的空间结构模型估计方法。该方法基于反演思想,通过最小化随机模型与测井数据和地震数据的空间结构差异,分别估计空间结构模型的纵向自相关长度和横向自相关长度。同时,为了优化空间结构模型的估计效果,在纵向自相关长度的反演过程中引入边界保护正则化,以提高反演的稳定性。此外,将地震约束引入模型优选以提高随机模型的稳定性。实验结果表明:该方法能够稳定估计地下介质的非平稳空间结构模型,从而建立准确描述复杂储层非平稳空间相关特征的高分辨率随机模型。与基于序贯高斯协模拟的随机建模方法相比,使用空间结构模型优化的FFT-MA随机建模方法能够有效刻画多种复杂地质构造从而实现复杂储层建模。

Microstructural evolution in Al–Zn–Mg–Cu–Sc–Zr alloys during short-time homogenization

Abstract： Microstructural evolution in a new kind of aluminum （A1） alloy with the chemical composition of AI-8.82Zn-2.08Mg- 0.80Cu-3.31Sc-0.3Zr was investigated. It is found that the secondary phase MgZn2 is completely dissolved into the matrix during a short homogenization treatment （470℃, 1 h）, while the primary phase A13（Sc,Zr） remains stable. This is due to Sc and Zr additions into the A1 al- loy, high Zn/Mg mass ratio, and low Cu content. The experimental findings fit well with the results calculated by the homogenization diffusion kinetics equation. The alloy shows an excellent mechanical performance after the short homogenization process followed by hot-extrusion and T6 treatment. Consequently, a good combination of low energy consumotion and favorable mechanical properties is obtained.

Differentially Expressed Proteome of Microcystis under Short-time Ultrasonic Stress

This study was conducted to explore the regulation mechanism for key protein expression. The Microcystis treated by short-time ultrasonic wave was select-ed to analyze the total protein based on 2-DE. The results showed that there were 71 up-regulated protein spots, 56 down-regulated protein spots, 54 new protein spots and 21 protein spots disappeared under short-time ultrasonic stress. Eight dif-ferential proteins were chosen for further MALDI-TOFTOF/MS analysis, and the re-sults showed that 2 unknown proteins and 6 functional proteins were detected. These proteins were relevant to some physiological processes, such as antioxidation and anti-inflammatory process, phosphate synthesis and electron transfer, which is beneficial to the metabolic balance and self-protection under short-time ultrasonic stress.

基于PMF-FFT的北斗B2a信号捕获算法研究

为了对北斗三号B2a信号进行快速且准确地捕获,提出了一种基于PMF-FFT的捕获算法。首先,通过建立数学模型,分析了PMF-FFT算法的特性;然后,对PMF分段长度、FFT运算点数、相关积分时间、非相干积分次数和判决门限等参数的选取进行了详细地分析;最后,利用Matlab工具对PMF-FFT捕获算法性能进行了仿真与分析,验证了算法的有效性,为算法的实现解决了首要问题。

基2-FFT输入分级截断算法在频域合成孔径超声成像中的研究

为提高超声频域成像算法的计算速度,提出一种应用于超声频域成像算法的基2-FFT输入分级截断算法。首先,借助于COMSOL多物理场仿真软件,建立钢件中含有孔缝缺陷的有限元模型进行声场仿真。仿真结果得到关于缺陷的回波信号,并通过PSM算法对频域内声场进行重建,得到成像区域的聚焦图像,和原始仿真信号的B扫图像相比效果更加直观且成像质量更好,验证了PSM算法的可行性。然后为了避免超声频域成像算法中二维傅里叶变换的冗余计算,进一步提出了支持任意非0值输入的基2-FFT输入分级截断算法。实验结果证明,基2-FFT输入分级截断算法比标准基2-FFT算法快27%,超声频域算法成像速度提高13%。

改进FFT算法在频域全聚焦超声成像中研究

针对钢块中孔洞缺陷的超声成像检测,目前普遍采用时域超声成像算法。该方法中的延时叠加过程需要进行重复、繁琐的迭代运算,无法满足高质量实时成像的需求,频域超声成像算法具有更高的成像分辨率和更快的成像速度。提出了一种针对任意非0值输入的基2-FFT输入分级截断算法,将该算法应用在频域FMC-PSM算法成像中,得到改进的FMC-PSM算法,用于钢块中孔洞缺陷检测超声成像。实验结果证明,相较于标准FMC-PSM算法,改进的频域FMC-PSM算法能够在呈现更高质量图像的同时将成像速度提高了15%,有望解决超声成像中成像质量和成像效率难以兼顾的问题。

Adaptive Short-Time Fractional Fourier Transform Based on Minimum Information Entropy

Traditional short-time fractional Fourier transform(STFrFT)has a single and fixed window function,which can not be adjusted adaptively according to the characteristics of fre-quency and frequency change rate.In order to overcome the shortcomings,the STFrFT method with adaptive window function is proposed.In this method,the window function of STFrFT is ad-aptively adjusted by establishing a library containing multiple window functions and taking the minimum information entropy as the criterion,so as to obtain a time-frequency distribution that better matches the desired signal.This method takes into account the time-frequency resolution characteristics of STFrFT and the excellent characteristics of adaptive adjustment to window func-tion,improves the time-frequency aggregation on the basis of eliminating cross term interference,and provides a new tool for improving the time-frequency analysis ability of complex modulated sig-nals.

An HHT-based method to eliminate short-time strong disturbance from measured signals of bridge

According to the characteristic that Hilbert-Huang transform （HHT） can detect abnormity in signals, an HHT-based method to eliminate short-time strong disturbance was proposed. The signal with short-time strong disturbance was decomposed into a series of intrinsic mode functions （IMFs） and a residue by the empirical mode decomposition （EMD）. The instantaneous amplitudes and frequencies of each IMF were calculated. And at abnormal section, instantaneous amplitudes and frequencies were fired according to the data at normal section, replacing the fitted data for the original ones. A new set of IMFs was reconstructed by using the processed instantaneous amplitudes and frequencies. For the residue, abnormal fluctuations could be directly eliminated. And a new signal with the short-time strong disturbance eliminated was reconstructed by superposing all the new IMFs and the residue, The numerical simulation shows that there is a good correlation between the reconstructed signal and the undisturbed signal, The correlation coefficient is equal to 0.999 1. The processing results of the measured strain signal of a bridge with short-time strong disturbance verify the practicability of the method.

Characteristics of Radar Echo Parameters and Microphysical Structure Simulation of a Short-Time Heavy Precipitation Supercell

By using the conventional observations, radar data, NCEP/NCAR FNL 1°×1° reanalysis data and numerical simulation data and with the construction and calculation of radar echo parameters, this paper presents the structural characteristics and physical processes of a short-time heavy precipitation supercell that occurred in the squall line process in Shanxi Province on 24 June 2020. The results show that this squall line event occurred in front of a surface cold front,combined with infiltration of low-level cold air and continuous increase of near-surface humidity in the afternoon. The surface mesoscale convergence line and mesoscale dew point front contributed to the development and systemization of the squall line by a large degree. The short-time extremely heavy precipitation in Pingshun County was caused by the development of a supercell from thunderstorm cells on the front side of the squall line. The characteristics of sharp increase in vertical integral liquid water content, persistent increase in reflectivity factor and continuous rise in the echo top height appeared about 23 min earlier than the severe precipitation, which has qualitative indicating significance for the nowcasting of short-time heavy precipitation. A quantitative analysis of the radar echo parameters suggests that the“sudden drop”of FV40was a precursor signal of cells’ coalescence and rapid development to the mature stage. The areal change of the echo core at the 6 km height was highly subject to the merging and developing of cells, the rapid change of hydrometeor particles in clouds and the precipitation intensity. Changes in the cross-sectional area of convective cells at different heights can indirectly reflect the changes of liquid particles and ice particles in clouds, which is indicatively meaningful for predicting the coalescing and developing-to-maturing of cells and heavy precipitation 30-45 min earlier.A comprehensive echo parameter prediction model constructed by the random forest principle can predict the magnitude of short-time heavy precipitation 40-50 min in advance. Numerical simulation reveals that large amounts of water vapor existed in the near-surface atmosphere, and that the cells rapidly obtained moisture from the ambient atmosphere and developed rapidly through maternal feeding. The cold cloud zone was narrow, upright and had a high stretch height. The upward motion in clouds was strong and deep, and very rich in liquid water content. The graupel particles had a large vertical distribution range, the coexistence area of graupel and snow was large, the height of raindrops was close to the surface with a wide horizontal scale, and the precipitation efficiency was high. These may be the important elements responsible for the occurrence of the short-time heavy precipitation that exceeded historical extreme values. On the basis of the above analyses, a comprehensive parameter(CP) prediction model is worked out, which can estimate the developing trend of supercells and the intensity of short-time heavy precipitation about 1 h in advance.

Characteristics Analysis on Short-Time Heavy Rainfall during the Flood Season in Shanxi Province, China

In order to provide a reference for the correct forecasting of short-term heavy rainfall and better disaster prevention and mitigation services in Shanxi Province, China, it is very important to carry out systematic research on short-term heavy precipitation events in Shanxi Province. Based on hourly precipitation data during the flood season (May to September) from 109 meteorological stations in Shanxi, China in 1980-2015, the temporal and spatial variation characteristics of short-time heavy rainfall during the flood season are analyzed by using wavelet analysis and Mann-Kendall test. The results show that the short-time heavy rainfall in the flood season in Shanxi Province is mainly at the grade of 20 - 30 mm/h, with an average of 97 stations having short-time heavy rainfall each year, accounting for 89% of the total stations. The short-time heavy rainfall mainly concentrated in July and August, and the maximal rain intensity in history appeared at 23 - 24 on June 17, 1991 in Yongji, Shanxi is 91.7 mm/h. During the flood season, the short-time heavy rainfalls always occur at 16 - 18 pm, and have slightly different concentrated time in different months. The main peaks of June, July and August are at 16, 17 and 18 respectively, postponed for one hour. Short-time heavy rainfall overall has the distribution that the south is more than the north and the east less than the west in Shanxi area. In the last 36 years, short-time heavy rainfall has a slight increasing trend in Shanxi, but not significant. There is a clear 4-year period of oscillation and inter-decadal variation. It has a good correlation between the total precipitation and times of short-time heavy rainfall during the flood season.

基于FFT-STL的大坝监测数据时效分量提取与分析算法

研判监测物理量的变化趋势是否向危险方向发展是大坝监测资料定量分析的主要目的,但传统方法易受人为因素的影响。为此,提出了一种基于FFT-STL的大坝监测数据时效分量提取与分析算法,该算法采用FFT算法计算监测数据的周期,并将其作为输入参数传递给STL分解算法,自动提取数据的时效分量,采用不同类型的时效分量模型分别回归拟合优选最佳模型,最后依据变化速率对时效分量的趋势进行研判。经实例分析证明,与传统算法相比,所提算法能准确可靠地反映大坝安全监测数据的变化和发展趋势,且具有更好的适应性。

基于DIC技术和贝叶斯FFT方法结合的结构位移监测和模态参数识别

文章基于计算机视觉技术拍摄的一系列结构振动图像,运用数字图像相关(DIC)技术获取监测节点的亚像素振动位移数据,在此基础上,采用快速贝叶斯FFT方法对被测试结构的动力模态参数进行辨识。为了验证计算机视觉结构振动测试和快速贝叶斯FFT方法相结合在结构模态参数识别中的有效性和准确性,以某实验室5.6 m跨钢桁架模型的振动视频为算例,针对结构有无构件损伤等6种不同工况条件下的振动视频数据,进行振动位移的提取,进而分析桁架不同位置处监测节点识别位移与实际位移之间产生的误差大小及其原因,并借助快速贝叶斯FFT方法,对从不同部位采集的振动视频数据进行结构动力模态参数识别结果的精确度和不确定性对比分析。实验结果表明,文章将DIC位移测量技术和贝叶斯FFT方法相结合,能够有效实现对结构动力模态参数的精准识别。

全相位FFT算法高精度谐波测量方法

针对电力系统精确测量谐波信号频谱特性这一技术问题,该文提出一种基于全相位FFT算法的高精度谐波测量方法。通过低漂移信号转换电路和程控滤波器对被测信号进行调理,再经高精度A/D转换器变换成数字量传至MCU进行全相位FFT变换,选择离散频谱中幅值最大的两根谱线进行校正得到信号精确测量结果。利用LTspice电路与Matlab算法协同仿真,结果表明:在输入信号为理想谐波、复杂谐波与间谐波、叠加白噪声信号的基波情况下,所提出的方法均可实现对信号频率和幅值的准确测量。在与传统FFT双谱线校正和加窗FFT双谱线校正相比,该文提出的测量方法精度更高、抗噪性更好,频谱泄漏抑制能力更强,适用于对电力信号的高精度测量。

基于Slepian和Parzen互卷积窗插值FFT的电力系统谐波分析方法

谐波是电力系统中最为突出的电能质量问题之一。谐波参数的准确估计是电力企业和用户评价电能质量的重要参考依据。目前,电网谐波参数的估计方法主要基于傅里叶变换算法,但该算法在分析和处理信号时需要对原始信号进行截断和离散化处理,会不可避免地导致频谱泄露和栅栏效应,从而严重影响电网谐波参数的准确测量。而具有优良旁瓣特性的窗函数以及插值算法可有效改善因频谱泄露和栅栏效应所带来的参数估计误差。为此,文中提出一种基于Slepian窗和Parzen窗的新型互卷积窗(slepian and parzen mutual convolution window,SPMCW)插值FFT的谐波分析方法。SPMCW具有较低的旁瓣电平和快速的旁瓣衰减速率,因而可有效抑制频谱泄露,进而实现弱谐波幅值的检测。在MATLAB下的仿真实验验证了文中算法的准确性和可靠性。实际构建的硬件平台验证了算法的有效性和可行性。