基于相量测量单元实测数据的变压器参数在线估计方法

给出了基于相量测量单元(PMU)实测数据的变压器变比(分接头位置)和电抗参数的计算方法.通过对变压器参数和变比的多时段计算结果进行统计分析发现,多时段参数计算结果符合正态分布.利用正态分布参数估计理论,给出了变压器参数和变比辨识结果的置信区间和点估计值,最大限度地减少了量测随机误差的影响.算法考虑了电力系统的生产实际情...     

基于主成分分析法的电力系统同调机群识别

提出了一种基于主成分分析(principal component analysis,PCA)的电力系统同调机群分群识别方法。利用PCA可以保留源数据中的主要信息,采用发电机角速度作为源数据,可以获取协方差矩阵及协方差矩阵的特征根和特征相量,由此确定发电机角速度的主成分,然后通过比较各发电机对主成分的载荷系数实现对发电机的同调分群。中国电力科学研究院36节点纯交流系统算例表明,该方法计算简单,易于实现,避免了模型参数对分群的影响。     

基于PMU和改进支持向量机算法的同调机群在线识别

鉴于现代电力系统中发生暂态稳定问题时,为了给后续的主动解列措施提供依据,需要快速准确地辨识出系统中的同调机群,基于同步相量测量单元（phasor measurement unit,PMU）实时采集的发电机动态轨迹信息具有高维度和非线性等特点,提出了一种在线识别同调机群的新方法：由PMU得到故障后发电机组的动态功角轨迹量测信息;对PMU量测信息进行标准化处理,生成标准化高维数据;利用留一交叉验证法确定Gauss径向基核函数参数g和惩罚系数C的最优取值,得到准确的分类器;使用此分类器对未知分类的样本进行分类,并得到最终的同调分群结果。仿真结果表明：该方法能有效克服传统方法识别准确率低和速度慢的缺点,能在线识别系统中的同调机群,且兼具识别的快速性和准确性,可满足现代电力系统暂态稳定的在线分析和实时计算等要求。     

基于流聚类的PMU异常数据辨识算法

为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。     

基于有限PMU的电网故障在线识别算法研究

为了实现有限PMU布局方式下的快速有效电网故障检测,研究了电网故障线路在线识别算法。根据PMU布局将电网划分为多个区域。分析了故障发生时流入故障区域的正序、负序和零序电流明显增大的特性,研究了基于边界PMU的故障区域搜索方法。基于虚拟节点构建了纯故障等值模型及其故障分量电压方程。利用一个区域的各边界节点电气量,构造故障分量电压的超定方程组,采用最小二乘法求解故障线路的故障点位置,准确检测故障区域内的故障线路。IEEE14节点系统上大量仿真实验验证了该算法的有效性,它不受故障位置、故障类型、过渡电阻的影响。仿真实验也表明了采用最小二乘法求解的故障点精度能够满足要求。     

基于DSCADA和μPMU数据融合的配电网运行拓扑辨识

在配电网安装了配电网数据采集及监视控制系统（distribution network supervisory control and data acquisition, DSCADA）和部分节点安装少量微型同步相量测量装置（micro-synchronous phasor measurement unit, μPMU）情形下,提出了一种基于DSCADA和μPMU遥测数据融合的配电网运行拓扑辨识方法。首先,基于μPMU节点电压相位量测构建配电网拓扑变化时刻辨识模型,确定拓扑变化的时刻;然后,基于拓扑变化前后的节点电压变化,借助DSCADA和μPMU的遥测数据构建可能拓扑判据,缩小重构后可能拓扑的范围;最后,使用加权最小二乘法将DSCADA和μPMU遥测数据进行融合,估计出可能拓扑下的节点电压相位,并利用构建的拓扑相似度辨识模型辨识出实际拓扑。算例中考虑μPMU和DSCADA不同量测误差组合,对该算法辨识的准确性进行验证。     

Robust intelligent PCA‐based face recognition framework using GNP‐fuzzy data mining

Traditional principal component analysis (PCA) based face recognition algorithms have a low recognition accuracy due to the influence of noise and illumination changes. This paper proposes a robust, intelligent PCA‐based face recognition framework in the complicated illumination database when using multiple training images per person (MTIP‐CID). There are mainly two improvements in the proposed method. One is that a face‐recognition‐oriented genetic‐based clustering algorithm is introduced to reduce the influence of a large number of classes on the classification accuracy in the MTIP‐CID. The other is that a classifier based on fuzzy class association rules (FCARs) is applied to mine the inherent relationships between eigenfaces and to improve the robustness of PCA‐based face recognition in noisy environments. Experimental results on the extended Yale‐B database demonstrate that the proposed framework performs better and is more robust against noise compared with other traditional face recognition algorithms, i.e. linear discriminant analysis (LDA) and local binary patterns (LBPs). © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

一种基于PMU和SCADA单节点互校核的前端数据辨识框架

随着电网自动化技术的发展,数据中心可获取海量多源多时空数据,在此基础上进行多源量测值互校核有利于实现后续大数据高级应用。针对单节点同时存在PMU与SCADA量测值的情况,提出一种前端不良数据辨识框架。为克服量测值负样本较少的问题,采用基于粒子群优化的改进一分类支持向量机辨识方法,根据两源量测差值识别异常点。对接近向量机边界可能被误判的值利用间隙统计法进行修正,确定不良数据。然后检验其所在时间点的PMU量测值,最终确定不良数据位置。基于某省实际电网数据对PMU与SCADA互校核辨识框架进行了验证与分析。计算结果表明所提方法能够有效地辨识出两数据源的前端不良数据,计算量小、耗时较短,比仅利用单源数据进行校核的结果更加可靠。     

基于相量量测的电力系统线性状态估计

分析了相量量测装置的量测误差情况,指出了相量量测参与状态估计计算的必要性。在完全使用相量量测的情况下,给出了基于直角坐标系的实数形式的电力系统线性量测方程和相应的线性静态状态估计算法。对负荷预报加潮流计算的系统状态预报方法进行改进,通过对误差协方差阵计算公式的推导与简化,提出了新的预报误差协方差阵计算公式,并将其与线性量测方程相结合,提出了基于相量量测的线性动态状态估计算法。最后讨论了线性状态估计算法的使用条件,并采用IEEE30节点系统对提出的算法进行了验证。     

Midterm stability evaluation of wide‐area power system using synchronized phasor measurements

In recent years, the PMU (Phasor Measurement Unit) has received a great deal of attention as a synchronized measurement system of power systems. Synchronized phasor angles obtained by the PMU provide valuable information for evaluating the stability of a bulk power system. The aspect of instability phenomena during midterm tends to be more complicated, and the stability analysis using the synchronized phasor measurements is effective in order to keep a complicated power system stable. This paper proposes a midterm stability evaluation method for the wide‐area power system using synchronized phasor measurements. By clustering the power system to some coherent groups, step‐out is predicted on the basis of an aggregated two‐machine equivalent power system. The midterm stability of a longitudinal power system model of Japan's 60‐Hz systems constructed by a hybrid‐type power system simulator is practically evaluated using the proposed method. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 147(1): 25–32, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10274     

基于同步测量信息的电网拓扑错误辨识方法

将相量测量单元（PMU）提供的同步测量信息引入到拓扑错误辨识中,提出了一种基于同步测量信息的拓扑错误辨识算法以提高电网拓扑分析的可靠性。该算法首先在对拓扑错误进行显性、隐性分类的基础上,构建错误辨识准则,然后结合图的矩阵运算,确定PMU所量测的开关量和模拟量之间的判别关系,最后根据判别结果,对各类拓扑错误进行有效辨识。仿真算例表明,该算法能够准确对单个拓扑错误、多个拓扑错误以及同时存在多个不良数据与多个拓扑错误的情况进行有效辨识,具有可靠性高、容错能力强等优点。     

A New Algorithm for Predicting Out-of-step Using Rotor Speed-acceleration Based on Phasor Measurement Units (PMU) Data

Transient instability is one of the major threats to system security which can cause out-of-step condition. Out-of-step condition can result in mechanical and thermal damage to generators. Therefore, in the case of out-of-step, early detection and disconnection of the generator from grid is essential. In this article, using generator rotor speed-acceleration (ω ? α) data obtained from phasor measurement units (PMU) measurements, a new algorithm for predicting out-of-step condition for generators is proposed. The trend of the movement of the (ω ? α) locus curve in the plane provides a measure for predicting and detecting out-of-step status. The predictive ability of this method enables early tripping of the unstable generator, thereby avoiding hazard damage. The proposed algorithm is examined using an IEEE 39 bus system. The simulation results demonstrate the ability of the proposed algorithm for correct prediction of various unstable power swing conditions, with sufficient early prediction compared with the actual instability time.     

Identification of characterization factor for power system oscillation based on multiple synchronized phasor measurements

Electric power systems in Japan are composed of remote and distributed location of generators and loads mainly concentrated in large‐demand areas. The structures having long‐distance transmission tend to produce heavy power flow with increasing electric power demand. In addition, some independent power producers (IPP) and power producer and suppliers (PPS) are participating in the power generation business, which makes power system dynamics more complex. However, there was little observation as a whole power system. In this paper the authors present a global monitoring system of power system dynamics by using the synchronized phasor measurement of demand‐side outlets. Phasor Measurement Units (PMU) are synchronized based on the global positioning system (GPS). The purpose of this paper is to show oscillation characteristics and methods for processing original data obtained from PMU after certain power system disturbances triggered by accidents. This analysis resulted in the observation of the lowest and the second lowest frequency mode. The derivation of eigenvalue with the two‐degree‐of‐freedom model brings a monitoring of two oscillation modes. Signal processing based on wavelet analysis and simulation studies to illustrate the obtained phenomena are presented in detail. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 10– 18, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20316     

不确定量测下发电机动态状态估计性能分析

相量测量单元（PMU）中随机误差不可避免,在实际电网系统中PMU量测数据可能出现延时、重新排序甚至丢失等不确定情况。为准确估计电力系统机电暂态过程中的状态信息,首先建立量测丢失下的发电机动态状态估计模型;然后在某实际电网系统算例中分别采用无迹混合滤波（UMF）、粒子滤波（PF）和所提出的改进粒子滤波（IPF）3种算法对发电机动态状态估计模型进行了仿真试验。仿真结果表明：在不确定量测系统下,改进的IPF算法的滤波性能和抗差性能优于UMF与PF算法,更适用于不确定量测下发电机动态状态估计。     

考虑数据缺失的电力系统暂态稳定自适应集成评估方法

随着电力物联网概念的提出,暂态稳定评估在电力系统规划运行中扮演着越来越重要的角色.由于同步相量测量单元(PMU)的广泛配置,基于机器学习和PMU在线量测数据的暂态稳定实时评估方法展现出了巨大的发展潜力.针对这类方法在应用中可能因PMU失效而严重影响精度的问题,文中提出了一种考虑数据缺失的电力系统暂态稳定自适应集成评估方法.首先,在保证全网节点可观性的基础上构建考虑PMU重要性的PMU子集集合搜索算法.然后,根据PMU子集对应的特征集训练暂态稳定评估子模型.最后,在任意可能的PMU失效情况下采用自适应加权融合机制构建集成暂态稳定评估模型.在新英格兰10机39节点电力系统上的仿真表明,文中提出的方法在PMU失效造成的数据缺失下仍然能够准确、可靠地进行暂态稳定评估,在鲁棒性、计算量及准确率上相比已有的方法均具有较大优势.     

基于HHT的同步电机参数辨识

将一种新的非平稳信号的处理方法—Hilbert-Huang变换(HHT)应用于同步电机的参数辨识中。提出了基于HHT方法的短路电流处理新方法,该方法以经验模态分解(empiricalmodedecomposition,EMD)为基础,构成一种新型的时空滤波方法,从强噪声背景下的短路电流数据中有效地提取出了基波分量和直流分量。克服了传统处理方法精度低的缺点,并且不存在小波基选取问题,具有处理精度高、自适应性强的特点。还提出了基于稳健回归算法的直流分量辨识算法,以及基于Hilbert变换和非线性变量优化(NLO)的基波分量辨识算法,实现了同步电机瞬态和超瞬态参数的精确辨识。用加入滤波环节的F-EMD,降低了电流分量分离过程中的计算量。仿真分析和试验数据分析验证了该方法的有效性。     

Development of a novel robust identification scheme for nonlinear dynamic systems

This paper presents a set of single layer low complexity nonlinear adaptive models for efficient identification of dynamic systems in the presence of outliers in the training signal. The weights of the new models have been updated using a new robust learning algorithm. The proposed robust algorithm is based on adaptive minimization of Wilcoxon norm of errors. The computational complexity associated with the new models has further been reduced by processing the input in block form and using a newly derived robust block learning algorithm. Through exhaustive simulation study of many benchmark identification examples, it has been shown that in all cases, the new models provide enhanced and robust identification performance compared with that provided by the corresponding conventional squared error‐based approaches. Copyright © 2014 John Wiley & Sons, Ltd.     

Generalized State Estimation of Flexible AC Power Systems Considering Wind Generators and Primary Frequency Control

This paper proposes a practical approach to incorporate the mathematical models of both fixed-speed and variable-speed wind turbine generators, automatic load frequency controls as well as voltage magnitude and frequency dependent loads into a weighted least squares-based state estimation algorithm suitable for the analysis of flexible alternating current (AC) transmission systems. As opposed to conventional static state estimators, where the inclusion of these electric components has been neglected so far, the proposed approach permits the determination of the steady state operation of a power system in the event of a supply-demand unbalance by estimating the magnitude of the frequency deviation from its nominal value. The state estimation is based on measurements related to those that should be obtained by a supervisory control and data acquisition (SCADA) system and phasor measurement units. For the purpose of this paper, the set of values associated with SCADA measurements (nodal power injections, power flows, and voltage magnitudes) and phasor measurement unit (PMU) measurements (voltage and current phasors) are generated from a power flow analysis of the network under study. Lastly, numerical simulations are reported to demonstrate the effectiveness of the proposed approach.     

Line Outage Detection Using Phasor Angle Measurements

Although phasor measurement units (PMUs) have become increasingly widespread throughout power networks, the buses monitored by PMUs still constitute a very small percentage of the total number of system buses. Our research explores methods to derive useful information from PMU data in spite of this limited coverage. In particular, we have developed an algorithm which uses known system topology information, together with PMU phasor angle measurements, to detect system line outages. In addition to determining the outaged line, the algorithm also provides an estimate of the pre-outage flow on the outaged line. To demonstrate the effectiveness of our approach, the algorithm is demonstrated using simulated and real PMU data from two systems—a 37-bus study case and the TVA control area.      

用PMU实测数据辨识同步发电机参数的关键问题

研究了参数的可辨识性分析、扰动类型的选择、相量测量单元量测数据的滤波和实测功角恒定偏差对同步发电机参数辨识的影响等问题。提出以H阵的条件数作为参数的可辨识性指标,用以定量描述参数的辨识条件,以同时考虑模型结构和扰动深度等因素对参数可辨识性的影响。从理论上分析了不对称扰动数据的正序分量可用于同步发电机的参数辨识,并用实测数据进行了验证。根据对发电机动态特性的激发程度,合理地选择扰动类型;从电子信息科学领域引入一种零相移的数字滤波方法,解决了传统滤波器给量测信号引入相移的问题。总结了现有的功角测量方法,指出直接法测得的功角与真实功角可能存在恒定偏差,并研究了这一偏差对参数辨识结果的影响。实际算例验证了所述方法的有效性。