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.     

塔里木盆地顺托果勒地区志留系柯坪塔格组米兰科维奇旋回沉积记录

塔里木盆地志留系柯坪塔格组主要沉积于盆地构造活动相对稳定期的潮坪环境，是盆地内典型的海相碎屑岩地层之一，由多层中-细砂岩和泥岩频繁互相叠置而成，地层分布上具有明显的三段特性。根据前人已有研究成果，柯坪塔格组在国际地层表上对应于特列奇阶、埃隆阶及鲁丹阶，沉积时限应介于8.1~12.7 Ma。然而目前对其米兰科维奇高频地层旋回格架划分鲜有探讨，通过选择顺托果勒地区顺9井自然伽马测井曲线作为替代指标，运用频谱分析法进行计算。结果表明顺9井柯坪塔格组记录并保存了完整的米兰科维奇旋回，天文旋回周期在地层沉积过程中对旋回厚度具有明显的控制作用。其中旋回厚度16.73 m对应于405 ka长偏心率周期；5.24 m和3.92 m分别对应于125 ka和95 ka的短偏心率周期；1.59 m和1.26 m分别对应于38.1 ka和31.1 ka的斜率周期。地层平均沉积速率为4.15 cm/ka，沉积时限为10.4 Ma，通过经验模态分解法建立了顺9井柯坪塔格组"浮动"天文年代标尺。柯坪塔格组共记录了83个125 Ka短偏心率旋回，334个31.1 ka周期斜率旋回。最后结合Fischer图解法，以125 ka短偏心率周期作为控制的五级旋回划分的标准模式，分析各个高频旋回之间的叠加差异及成因。该结果为盆地内或类似盆地海相碎屑岩高频旋回划分提供了一种统一的，不受人为影响且行之有效的新方法。     

EMD-based random decrement technique for modal parameter identification of an existing railway bridge

Vibrational measurement data are often nonstationary and modal parameter identification based on these data is of practical value for structural health monitoring and condition assessment. The empirical mode decomposition (EMD) is a most recent tool for analysis of nonstationary signals. An EMD-based random decrement (RD) technique is presented to identify modal parameters from monitoring vibrational data. The nonstationary measurement data are first decomposed into a series of quasi-stationary intrinsic mode functions (IMFs) by EMD. The RD technique is then applied to the selected IMFs to obtain the free-decay response. The modal frequencies and damping ratios are finally identified from the free-decay response by minimizing the error between the measured free-decay responses and the predicted responses from a parametric model. The present method is applied to extract the modal parameters of the Nanjing Yangtze River Bridge from the measured responses. The identification result is compared to those from finite element analysis as well as from the experimental result identified with the peak-picking (PP) method. In addition, the modal frequencies of the bridge loaded with heavy trains are also identified and compared to the ‘empty’ bridge. The EMD-based random decrement (RD) technique provides an effective and promising tool for modal parameter identification for large bridges and other structures.     

复杂环境下结合EMD的GPS-IR水位反演方法

利用法国布雷斯特(Brest)港BRST测站和英国塞文大桥监测系统GNSS双频观测数据,分别在静态和高动态环境下进行GPS-IR水位反演,探究传统GNSS监测系统进行水位反演的可行性与精度.结果表明:L1波段反演精度高于L2波段;在静态场景下,GPS-IR水位反演结果与验潮站数据相关系数大于0.98,在高动态场景下,桥梁GPS-IR水位反演精度稍低.利用经验模态分解(EMD)方法对算法进行改进,提高了在桥梁复杂环境下GPS-IR水位反演结果的精度,均方根误差(RMSE)相比经典方法降低约50%.本文方法提高了GPS-IR技术在不同水域环境下的适用性,在水位监测中具有很好的应用前景.     

基于EMD-SA-SVR的超短期风电功率预测研究

针对风电功率的高随机和强波动性,提出一种基于EMD-SA-SVR的风电功率超短期预测方法。采用经验模态分解(Empirical Mode Decomposition, EMD)提取风电功率序列的不同特征。将原始序列分解为多个更具规律的模态,针对每个模态序列建立各自的预测模型,以消除不同特征之间的相互影响。鉴于支持向量回归(Support Vector Regression, SVR)好的泛化能力,研究建立基于SVR的各模态预测模型。进一步采用模拟退火(Simulated Annealing,SA)算法对SVR参数进行优化以解决模型选择的多极值复杂非线性问题,获得各模态分量的最优模型,进而汇总各模态分量的结果得到风电功率预测值。在某风电场历史数据上的对比分析表明,EMD-SA-SVR模型可以有效提高风电功率超短期预测精度。     

基于EMD的某火炮身管模态参数识别

现阶段火炮模态参数识别都是以快速傅里叶变换(FFT)为基础,信号必须平稳且是严格的周期信号;通常情况火炮工作状态下的信号是非平稳信号;经验模式分解(EMD)方法是一种新的非平稳的信号处理技术,EMD方法在处理非平稳信号时相对其他方法有相当大的优势;首先对某火炮身管在有限元分析软件中建模,分别在有约束和自由状态下对身管进行模态识别;然后用EMD方法对相关数据进行处理,得到身管的模态参数(频率、阻尼比、刚度矩阵、阻尼矩阵、质量矩阵),并与有限元分析方法得到的结果进行对比、分析;数值仿真证明了EMD方法在火炮领域的可行性和有效性.     

超声信号的改进EMD阈值方法降噪研究

在进行工业超声无损检测的过程中,由于环境、仪器等原因产生噪声,对后续缺陷分析等工作形成一定干扰,不利于完成后期数据分析。因此为了使信号的信噪比有所提高,提出了一种改进的经验模态分解阈值降噪算法。同时对比小波软阈值降噪法与经验模态分解清除重复间隔阈值的方法,在MATLAB仿真软件中建立超声回波数学模型并进行算法验证。实验结果表明,经验模态分解降噪方法优于小波阈值降噪,改进后的经验模态分解阈值降噪方法在信噪比、均方误差、光滑度3个方面均有所提高,达到了较好的降噪效果。     

基于分形维数的PMSM局部退磁故障诊断

为了实现永磁同步电机(PMSM)永磁体局部退磁故障诊断,首先建立计及永磁体局部退磁故障的PMSM数学模型,实现永磁体局部退磁故障电气特性的定性与定量描述。在此基础上,提出了集成自适应基波提取和经验模态分解(EMD)于一体,基于分形维数的永磁体局部退磁故障诊断方法。最后,通过PMSM驱动系统稳态和动态运行工况对所提出的永磁体局部退磁故障诊断方法进行仿真测试和实验验证,综合系统仿真和实验验证结果证实了该方法的可行性和有效性。     

基于EMD时频分析方法的性能研究

针对传统的时频分析方法存在的局限性,评述了一种最新的可适用于非平稳、非线性信号的时频分析法-Hilbert-Huang时频分析.研究了经验模态分解(Empirical Mode Decomposition,EMD)与希尔伯特变换(Hilbert Transformation,HT)相结合的提取信号瞬时特征的EMD/HS法,并对其性能进行了分析.仿真实验中通过与传统的时频分析效果的比较,表明该法具有理想的时频聚集性,并且消除了交叉干扰项.     

基于EMD-小波包的随钻测量信号去噪方法

为了降低随钻测量过程中噪声对信号的影响,针对经验模态分解(EMD)去噪方法粗糙和小波包去噪方法缺乏自适应的问题,提出了一种基于EMD-小波包的随钻测量信号去噪方法。利用EMD分解自适应的特点将随钻测量信号分解成几个IMF分量;根据信号自相关函数的特性找出主要含噪的IMF分量,再利用小波包阈值去噪方法将含噪IMF分量中的噪声去除;将去噪后的IMF分量和剩余的分量重构,得到去噪后的随钻测量信号。为验证方法的有效性,进行了随钻测量实验并用该方法对采集的信号进行处理。结果表明该方法能够很好地去除随钻测量信号中噪声的干扰,提高信号的信噪比。