Hanning窗在电力系统谐波分析中的应用

采用FFT算法对电网信号进行谐波分析时很难做到同步采样和整数周期截断,由此造成的频谱泄漏和栅栏效应将影响到谐波分析的结果。本文应用矩形窗和Hanning窗的加窗插值FFT算法分析非同步采样的电力系统谐波,经过MATLAB仿真证明：采用基于Hanning窗的加窗插值FFT算法能够大幅度降低由非同步采样造成的误差,最后给出了实现该算法的C语言程序。     

基于时域平均的Prony法在间谐波检测中的应用

为了提高在低信噪比下的间谐波分析的精确度,提出了将时域平均法与Prony谱估计法相结合,以消除整数次谐波成分和间谐波成分的相互作用以及噪声对频谱估计的影响.首先通过时域平均抑制间谐波成分和噪声,分别采用奇数倍工频周期的时间窗和奇数倍工频周期的一半时间窗截取信号并加以扩展后,使用Prony法计算奇数、偶数次谐波成分;然后从原始信号中滤除整数次谐波成分,使用Pro-ny法计算间谐波成分,模型阶数降低.仿真结果表明,该方法可以在噪声环境下检测出间谐波,且估计出的谐波、间谐波频率精度较高,能满足实际应用的需要.     

基于加新型自卷积窗的频谱插值谐波分析方法

为了解决快速傅里叶变换在电力谐波分析方法中存在采样的不同与非整数周期截取而造成的栅栏效应和频谱泄露的问题,提出一种基于P阶三角自卷积窗改进FFT频谱插值的电力系统谐波分析方法。首先使用P阶自卷积窗截取信号,之后选取幅值最大的频率附近的离散的频谱3条谱线进行加权平均计算来确定谐波谱线的准确位置,进而可以得到谐波的幅值、相位和频率,最后通过多项式拟合的方法,提出谐波修正的公式。通过仿真分析,所提出的自卷积窗函数能降低频谱泄露和栅栏效应带来的影响。采用改进的频谱插值算法可以提高电力谐波的检测精度,有助于对谐波的应用分析。     

网络化虚拟谐波分析仪的设计与实现

本文在对传统的谐波分析方法进行比较之后,提出了一种结合FFT和小波的改进型谐波分析算法,既可以测量出稳态谐波分量的具体参数值,又能提取出谐波信号中的非稳态成分.并在LabVIEW7.1和MATLAB6.5平台上设计并实现了一网络化虚拟谐波分析仪.系统采用C/S结构,可以通过Internet网采集谐波数据,实时显示,并利用上述谐波分析算法进行谐波分析,达到了良好的谐波分析效果.该网络化虚拟谐波分析仪较之传统的硬件仪器具有精确度高,直观性强,可联网,性价比高,易于功能扩展的优点,应用前景良好.     

基于正交三角函数族的电力系统谐波识别算法

谐波是影响电能质量的重要因素,对谐波进行监测和分析具有重要的意义.基于傅立叶变换与小波变换的算法是目前电力系统谐波分析最常见的分析方法,但两者都存在频谱混叠问题.为了克服传统谐波算法的频谱混叠从而提高检测精度,根据电力系统信号的特点设计了一种新的基于正交三角函数族的谐波识别算法.算法不存在谱间干扰,能得到高精度的稳态谐波分量的幅值与相位信息.通过与FFT、加窗FFT以及小波包算法的对比,算法具有实现简单,分析精度高的优势.仿真试验验证了该算法的可行性.     

网络化虚拟谐波分析仪的设计与实现

本文在对传统的谐波分析方法进行比较之后,提出了一种结合FFT和小波的改进型谐波分析算法,既可以测量出稳态谐波分量的具体参数值,又能提取出谐波信号中的非稳态成分。并在LabVIEW7.1和MATLAB6.5平台上设计并实现了一网络化虚拟谐波分析仪。系统采用C/S结构,可以通过Internet网采集谐波数据,实时显示,并利用上述谐波分析算法进行谐波分析,达到了良好的谐波分析效果。该网络化虚拟谐波分析仪较之传统的硬件仪器具有精确度高,直观性强,可联网,性价比高,易于功能扩展的优点,应用前景良好。     

基于卡尔曼滤波的谐波检测分析

针对日益严重且复杂的电网谐波问题,提出了一种基于卡尔曼滤波的谐波检测方法.阐述了卡尔曼滤波器的跟踪估计原理,并建立了基于卡尔曼滤波器谐波分析的数值模型,结合Matlab仿真平台对算法检测稳态谐波和暂态谐波信号的性能进行了分析.通过对比研究表明该算法在准确度、快速性、暂态谐波分析等方面均优于快速傅里叶变换(FFT),仿真和试验结果表明该算法在分析复杂的电网电能质量事件中具有较高的实用性.     

基于加窗插值FFT和APFFT的谐波检测算法

研究加窗插值傅里叶变换(加窗插值FFT)和全相位傅里叶变换(APFFT)在电网谐波分析中的应用.详细分析了频谱泄漏效应对测最精度的影响.通过数值模拟,发现加窗插值FFT对信号的幅值和频率的检测精度很高,但对相位的检测还存在着比较大的误差,而APFFT具有相位不变性的特征,能精确地提取相位信号.将加窗捅值FFT用于幅值、频率的检测,将APFFT用于相位的检测,通过试验仿真运行表明,以上的分析结果,电网谐波的频率、幅度、相位精度都很高,达到了国家标准.     

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。     

加过零检测的电力谐波加窗算法

离散傅里叶变换是电力谐波检测的有效方法。针对该算法频谱泄漏严重,导致谐波检测精度不高的问题进行了研究,分别采用对时域信号加平顶窗和经典海宁窗、对加窗信号进行傅里叶变换求解电力谐波的方法。考虑到加窗算法得到的谐波幅值的计算精度除了与所加窗的频谱特性有关外,还与采样初始相位密切相关,因此,提出先通过过零检测以决定信号初始采样相位,然后对采样信号加海宁窗进行谐波计算的方法。Matlab仿真和实际应用表明,该方案对于电力谐波检测具有较高的精度和实时性。     

半正弦窗的衍生形式及其特征分析

为得到更加精确的幅值或频率，本文提出了一种新的窗函数--衍生半正弦窗。它的构造方法是对半正弦窗函数施加一个指数，指数的大小阅览室它扁平程度。指数趋于0则窗函数趋于矩形窗，指数变大则趋于尖锐。通过数字方式研究了衍生半正弦窗的特性，发现它具有更大的灵活性，根据对频率分辨率和幅值精度、旁瓣衰减的侧重，可以构造一个需要的窗函数。为方便使用和对比，给出了衍生半正弦窗及其他常用窗函数的参数表。文中给出一个例子，对一个复信号用衍生半正弦窗进行加窗处理，用FFT计算它的幅值谱，结果证明衍生半正弦窗具有比较理想的特性。     

自动选择工具在通讯软件中的应用研究

论文设计了一种基于Windows窗口句柄的自动选择工具,该工具在即时通讯软件中具有一定的应用价值,它可以迅速准确地帮助用户定位聊天对象。该工具利用唯一标识窗口句柄绑定目标窗口,待系统收到WM_COMMAND消息后根据存储的Windows窗口句柄设置绑定窗口为当前活动窗口,并把键盘焦点设置到该窗口上。该工具利用ini文件保存最后一次绑定的窗口名称。当程序中存储窗口句柄的变量为空时,根据ini文件中的记录绑定目标窗口。     

在VFP中使用Screen对象定制系统窗口

Visual FoxPro(VFP)系统窗口既是用户操作的窗口,也是系统的输出窗口.如何定制VFP系统窗口随用户的不同而不同.在VFP 6.0环境中,通过使用Screen对象定制系统窗口的论述,得出定制VFP的系统窗口的方法.定制方法适用于VFP 6.0及其后续版本.     

Towards a variable size sliding window model for frequent itemset mining over data streams

Sliding window is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. The main idea behind a transactional sliding window is to keep a fixed size window over a data stream. The window size is kept constant by removing old transactions from the window, when new transactions arrive. Older transactions of window are removed irrespective to whether a significant change has occurred or not. Another challenge of sliding window model is determining window size. The classic approach for determining the window size is to obtain it from the user. In order to determine the precise size of the window, the user must have prior knowledge about the time and scale of changes within the data stream. However, due to the unpredictable changing nature of data streams, this prior knowledge cannot be easily determined. Moreover, by using a fixed window size during a data stream mining, the performance of this model is degraded in terms of reflecting recent changes. Based on these observations, this study relaxes the notion of window size and proposes a new algorithm named VSW (Variable Size sliding Window frequent itemset mining) which is suitable for observing recent changes in the set of frequent itemsets over data streams. The window size is determined dynamically based on amounts of concept change that occurs within the arriving data stream. The window expands as the concept becomes stable and shrinks when a concept change occurs. In this study, it is shown that if stale transactions are removed from the window after a concept change, updated frequent itemsets always belong to the most recent concept. Experimental evaluations on both synthetic and real data show that our algorithm effectively detects the concept change, adjust the window size, and adapts itself to the new concepts along the data stream.     

卫星时间窗口计算的动态步长快速算法

针对卫星轨道连续跟踪采样的时间窗口传统计算方法计算量大、效率低的问题,提出了一种新的快速算法。为减少参与计算的采样点数量,算法通过预测参与计算对象之间距离动态调整采样步长;为使算法适于解决各类时间窗口计算问题,提出广义可视概念进行时间窗口判定。分别研究了卫星与地面点目标可见时间窗口、星间可见时间窗口、卫星对地面目标覆盖时间窗口、地面大范围区域卫星过境时间窗口的广义可视判断方法和预测距离计算模型。实验结果表明,算法与传统算法精度完全一致,效率提升约99.7%。     

An adaptive modeling and asset allocation approach to financial markets based on discrete microstructure model

In previous works, it was verified that the discrete-time microstructure (DTMS) model, which is estimated by training dataset of a financial time series, may be effectively applied to asset allocation control on the following test data. However, if the length of test dataset is too long, prediction capability of the estimated DTMS model may gradually decline due to behavior change of financial market, so that the asset allocation result may become worse on the latter part of test data. To overcome the drawback, this paper presents a semi-on-line adaptive modeling and trading approach to financial time series based on the DTMS model and using a receding horizon optimization procedure. First, a long-interval identification window is selected, and the dataset on the identification window is used to estimate a DTMS model, which will be used to do asset allocation on the following short-term trading interval that is referred to as the trading window. After asset allocation is over on the trading window, the length-fixed identification window is then moved to a new window that includes the previous trading window, and a new DTMS model is estimated by using the dataset on the new identification window. Next, asset allocation continues on the next trading window that follows the previous trading window, and then the modeling and asset allocation process will go on according to the above steps. In order to enhance the flexibility and adaptability of the DTMS model, a comprehensive parameter optimization method is proposed, which incorporates particle swarm optimization (PSO) with Kalman filter and maximum likelihood method for estimating the states and parameters of DTMS model. Based on the adaptive DTMS model estimated on each identification window, an adaptive asset allocation control strategy is designed to achieve optimal control of financial assets. The parameters of the asset allocation controller are optimized by the PSO algorithm on each identification window. Case studies on Hang Seng Index (HSI) of Hong Kong stock exchange and S&P 500 index show that the proposed adaptive modeling and trading strategy can obtain much better asset allocation control performance compared with the parameters-fixed DTMS model.     

一种去除椒盐噪声的自适应极值中值滤波算法

针对椒盐噪声的特点,为了更好地滤除图像中的椒盐噪声同时又能较好地保护图像细节,提出一种自适应极值中值滤波算法。该算法通过对窗口内的非噪声点的检测自适应调整窗口大小,使用Max-Min算子作为噪声检测器,通过设置合理的阈值对灰度值等于极大值或者极小值的窗口中心的像素点进行噪声识别,减小将信号点误判为噪声点的概率,然后将检测出的噪声点用窗口内信号点的中值代替,而信号点保持不变直接输出。同时对超过设定的最大窗口的情况,窗口中心的像素点的灰度值用4个相邻的已处理的像素点灰度值的均值进行替换。实验仿真结果证明了该算法滤除椒盐噪声的有效性,在噪声较大时,去噪效果更明显。     

分块存储的滑动窗口数据重用技术

滑动窗口操作在基于可重构系统的典型应用（如图像处理、模式识别和数字信号处理等）中广泛采用,但是当前生成的滑动窗口电路,存在着存储冗余、操作等待等问题,导致执行效率不高。从增大滑动窗口数据吞吐量出发,提出了分块存储的滑动窗口数据重用方法,通过并行存取窗口数据,来减少存储器访问时间,加速滑动窗口执行。实验表明,在三个滑动窗口典型应用实例中,采用该方法生成的硬件电路能够将程序性能分别提高7.0~9.0倍。     

Efficient Window Block Retrieval in Quadtree-Based Spatial Databases

An algorithm is presented to answer window queries in a quadtree-based spatial database environment by retrieving all of the quadtree blocks in the underlying spatial database that cover the quadtree blocks that comprise the window. It works by decomposing the window operation into sub-operations over smaller window partitions. These partitions are the quadtree blocks corresponding to the window. Although a block b in the underlying spatial database may cover several of the smaller window partitions, b is only retrieved once rather than multiple times. This is achieved by using an auxiliary main memory data structure called the active border which requires O(n) additional storage for a window query of size n×n. As a result, the algorithm generates an optimal number of disk I/O requests to answer a window query (i.e., one request per covering quadtree block). A proof of correctness and an analysis of the algorithm's execution time and space requirements are given, as are some experimental results.     

PowerBuilder中父子窗口数据一致性编程

数据窗口是PowerBuilder的专利技术，在访问数据库和数据处理方面具有很强的功能，子数据窗口是一个与父数据窗口相连的数据窗口对象。本文以一个实例来说明如何在父窗口中连接子窗口，并且当父窗口数据改变时，子窗口数据亦随之改变，使父子窗口数据达到一致性。