EVM simulation and analysis in digital transmitter

The error vector magnitude (EVM) is extensively applied as a metric for digital transmitter signal quality compliance in modern communication systems. This article is focused on the effects of local oscillator (LO) phase noise and nonlinear distortion of power amplifier on EVM. This article contributes to below aspects. First, the relationships between EVM and two effects, LO phase noise and nonlinear distortion of power amplifier, are derived and expressed. Second, to simplify the expression, the third-order intermodulation distortion (IMD3) is used to calculate the EVM. Then, an expression for the EVM is derived based on the digital transmitter model that considers local oscillator phase noise and nonlinear distortion of power amplifier. Finally, the math formula of bit error rate (BER) versus EVM is given which can be easier and more useful to predict BER, according to analysis of the relationship between EVM and signal to noise rate (SNR), inspired by the works of Rishad, Md. Shahriar and AHM, 2006. Simulations are carried out to display the performance of EVM based on these relationships.     

基于EEMD与时间序列法的短期风电场功率预测

随着风力发电技术的发展,风电已成为最主要的新能源发电方式.但因风的随机性造成的风场输出功率的随机波动,电网将面对备用容量增多、调度难度增大以及风电场弃风等问题.解决上述问题的有效途径之一就是对风电场输出功率进行准确预测.针对风电场功率时间序列的非线性和非平稳性,分别将EMD和EEMD方法与时间序列的方法相结合应用于风电场功率预测中,提出基于EMD - ARMA和EEMD-ARMA的风功率预测方法.采用某风电场的实际功率数据进行分析预测,预测结果验证了所提方法的正确性和有效性.     

基于希尔伯特-黄变换（HHT）的机组同调研究

概述了利用功角曲线进行同调识别所面临的困难,阐述了希尔伯特-黄变换(HHT)在非线性、非平稳功角信号处理中的优势,提出了一种基于HHT的同调识别新方法。该方法利用经验模式分解方法将功角信号进行分解,得到剩余分量和各固有模态信号,逐次对这些分量进行比较得到机组的同调特性。该方法不受系统模型、故障场景限制,能随着不同的故障自适应调整数据时间窗的长度,并能克服小波分析、prony分析等算法难以处理非平稳信号的不足。测试结果表明了该算法的有效性。     

Zero-Sequence Voltage-based Method for Determination and Classification of Unloaded Overhead Line Operating Conditions at the Moment of Energization

Overhead line energization can be performed under normal or faulty conditions. The latter indicates an occurrence of a fault along the line that exists at the moment of energization. This can lead to significant over-voltages that could endanger proper line operation. The issue gets more complex to protective devices when it comes to high-resistance faults, which impair the ability of relays to react promptly and accurately. Consequently, installation of additional detection devices becomes necessary. This paper proposes a novel method that properly detects and classifies line operating conditions at the moment of energization. The method is designed to be useful for utilities and it can be considered as a low-cost, fast, and accurate detection and classification approach, suitable for dealing with both low-resistance and high-resistance faults. Through comprehensive mathematical modeling it was found that both normal and faulty conditions during line energization are accompanied by the zero-sequence voltages of specific characteristics. The differences between zero voltage sequences are reflected in harmonic content, magnitudes of dominant frequencies, and their phase angles in regard to supply voltage. These findings are taken as the method's detection and classification criteria. The validity of the proposed model is verified by simulations and by field measurements.     

基于经验模态分解的汽车动态称重数据处理

在论述经验模态分解(EMD)的基础上,提出了虚假模态的概念.讨论了虚假模态对信号稳态量的影响及其相应的处理方法,结合汽车称重信号的特点,应用EMD方法对实测信号进行了分解,对分解结果进行了分析和说明.5组100个称重信号的分解结果表明,EMD方法对汽车动态称重信号的处理是有效的,车速小于等于30km/h时前轴的偏差为5.23%,后轴偏差为10.30%,总重偏差为6.93%.     

EMD技术在动态称重数据处理中的应用

介绍了一种新的振动信号处理技术—EMD(经验模分解 )。根据EMD的特点 ,将用此方法处理动态称重系统输出信号得到的余量看作重量的稳定值。本文建立单自由度和二自由度称重系统的模型来验证EMD在动态称重信号处理上应用的可行性。计算结果表明 ,使用EMD处理单谐波、多谐波及含噪声的信号时都能得到较理想的结果 ,因此EMD是种可行的动态称重信号处理技术。     

Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMM and KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD feature combining with GMM and KELM based classifiers for the brainwave classification in terms of the Character-Writing application, which do not require any limb movement and stimulus.     

基于经验模态分解的爆破延期识别优化方法

针对爆破延期识别中采用经验模态分解(empirical mode decomposition, EMD)方法存在模态混叠现象,导致延期识别精度低的问题。提出了一种完全正交经验模态分解(principal empirical mode decomposition, PEMD)方法,首先对原始信号进行EMD初步分解,得到多个具有模态混叠现象的本征模函数(intrinsic mode function, IMF)分量,其次对IMF分量进行主成分分析(principal component analysis, PCA),将混叠的IMF分量完全正交化,之后选择幅值较大且波形衰减明显的主分量,使用Hilbert变换提取包络线,最后对包络线峰值点进行识别。通过相似物理模拟试验证明,PEMD与传统方法EMD相比,有效地抑制振动信号EMD分解时出现模态混叠现象,延期识别误差降低至0,并通过控制高程和延期时间对PEMD方法的稳定性进行了检验;同时以德兴露天边坡延期爆破试验为例,PEMD能够更好地对爆破振动波峰值点进行精确识别,识别率稳定在90%以上,对后续爆破工程中爆破参数设计优化和盲炮的识别具有重要意义...     

地震波基线漂移校正及结构地震响应分析

针对实际地震动采集过程中由于低频噪声、速度和加速度初始值以及人为操作误差等诸多影响因素造成地震波位移时程曲线出现严重基线漂移的问题,以地震波基线漂移校正为目标,在Iwan法和EMD法的基础上,提出了一种新的基线漂移处理方法。该方法可以保留原始记录的峰值和频谱特性,保证了基线漂移处理后的速度终点值为零,位移终点值平行于时间轴。在文中方法的基础上应用ANSYS结合"大质量法"和"位移法"进行了地震响应动力计算。结果表明:采用文中提出的基线漂移处理方法可以保证在不改变原始记录峰值及频谱特性的基础上,对地震波基线漂移现象进行校正。研究成果对低频噪声、初始值及人为操作误差等因素引起的基漂现象滤除具有参考价值。     

基于EMD的时标特征提取方法及其在短期电力负荷预测中的应用

为凸显负荷波动的随机性、周期性和相关趋势,通过探求负荷变化机理显著提升预测精度,本文提出了一种基于EMD的负荷波动机理研究方法。首先对负荷进行EMD分解,得到随机、周期和趋势分量;然后分析各分量的变化规律与候选影响因素的关联关系,推导负荷变化机理,提取时标特征值;最后,进行特征的去冗余。该方法创新点是能提取出特征值的时标特性。以广东省负荷数据集作为预测案例研究,对比实验研究结果表明本文所提出方法的有效性。