基于量测残差的改进参数估计方法

电网参数错误将影响状态估计的质量,并降低能量管理系统中其他高级应用软件的实用化程度。文中简要介绍了主要的参数估计算法,并详细分析了基于量测残差和参数误差间灵敏度关系的参数估计方法。在总体沿袭两步法参数估计方法的基础上,对该方法的近似模型进行了详细分析。为使算法具有更广的适用面,对第1步中偏差向量的计算和第2步中参数的估计方法分别进行了改进。对该算法进行了测试,结果表明,该方法具有良好的应用前景。     

考虑抗差状态估计大误差点的电网可疑参数辨识及其应用

错误的电网参数导致电力系统中状态估计结果出现偏差,严重影响各种高级应用的实用性。首先分析了一种能够自动排除不良数据对状态估计的影响且具有较高计算效率的抗差状态估计模型;然后提出了通过计算量测偏差与线路平衡状态来生成可疑线路的方法;最后采用变步长积分法估计可疑线路的参数修正值。通过在某省级电网调度中心的应用,实测结果验证了该方法的正确性与有效性。     

计及广域测量信息的状态估计错误参数识别与修正

因电网元件参数可能随工作环境变化而改变、遥信量在干扰下可能出现错误,需要对状态估计中的错误参数进行识别和修正。根据当前监控与数据采集（SCADA）系统和广域测量系统（WAMS）量测共存的状况,引入WAMS量测求得的功率残差和零注入节点功率残差,与SCADA量测残差一起构成拉格朗日函数,由优化理论得出参数误差与各残差的灵敏度关系,从而进一步识别和修正错误参数。所述方法不仅避免了增广参数估计维数高的问题,还利用了高精度的相量量测信息。在标准测试系统上的仿真结果验证了方法的有效性。     

考虑总体误差下降指标的电网参数辨识方法

电网参数错误是影响状态估计结果准确性的重要因素。文中以加权最小二乘状态估计为基础,分析了不良数据及错误参数集合对总体误差的影响,提出了基于总体误差下降指标的逐次型参数错误与不良数据辨识方法。该方法在辨识单个不良数据或参数错误时与正则化拉格朗日乘子法等价,并具备同时辨识多个不良数据及参数错误的能力。通过IEEE 14节点测试系统的仿真结果验证了所述方法的准确性与优越性。     

Improvements in Network Parameter Error Identification via Synchronized Phasors

This paper is concerned about the identification of network parameter errors, which are known to bias the state estimation solution. Previously proposed parameter error identification techniques implicitly assume the availability of a set of conventional measurements. In this paper, it is first shown that identification of certain parameter errors based on such conventional measurements, no matter how redundant they are, may not always be possible. The paper then describes the need for phasor measurements to overcome this limitation and presents an identification method that requires the use of such measurements. Examples are also included to describe the application of the proposed method to a number of typical topologies and measurement configurations.      

A bad data identification method for linear programming stateestimation

The author presents a bad-data identification procedure for linear programming (LP) power system static state estimation. LP state estimators minimize the weighted sum of the absolute values of the measurement residuals. The proposed procedure first detects the bad data using the measurement residuals of those measurements rejected by the LP estimator. Then the bad measurement is identified and eliminated by estimating the measurement errors of the zero residual measurements. The residuals obtained from this second estimation step are made use of for this purpose. In order to minimize the computational burden during the elimination cycles, a fast way of eliminating measurements through weight changing is also presented. The performance of the proposed procedure is tested and the results are presented, using AEP's 14, 30, 57 and 118     

A two steps procedure in state estimation gross error detection,identification, and correction

In this paper a two steps approach for the power system state estimation is proposed. The first step is the gross error detection test when all the measurements are assumed as possible of having errors. With that assumption, a rule for the measurement’s weights as being the inverse of a constant percentage of the measurement’s magnitudes is proposed. Then, using the error as the objective function of the state estimation (SE) process to be minimized, the gross error analysis is performed. In case a gross error is detected, the Composed Measurement Error, in its normalized form (CME^N), is used to identify the measurement(s) with error(s). The measurement(s) with error(s) is corrected using the Composed Normalized Error (CNE). In the second step, the state estimation is again performed, but using as the weight for each measurement the inverse of the measurement’s standard deviation as proposed in the classical estimators. In this step, the set of measures is the original set, but with the measures flagged with gross errors replaced by their estimated values. The IEEE-14, IEEE-57 bus systems and the 45-bus equivalent of the Southern part of Brazil are used to perform the tests.     

基于线性内点法及正交变换的抗差参数估计

参数估计目前主要采用加权最小二乘算法.由于包含参数估计,该方法在处理电网坏数据混杂及数值稳定性上都面临着困难.考虑到实际电网中有时会同时出现量测错误及参数错误,提出了使用基于线性内点法及正交变换的加权最小绝对值(WLAV)增广参数估计.基于L1范数的WLAV估计具有良好的抗差特性,用以应对混杂有坏数据、拓扑错误及参数错误的混杂估计.为提高增广参数估计的数值稳定性,将正交变换引入线性内点法修正方程的求解中.对算法的测试结果表明,该方法具有良好的应用前景.     

考虑电力参数和量测误差的网络拓扑误差辨识方法

电力系统的参数误差和量测误差在状态估计时经常同时发生。为此提出了一种同时辨识不正常状态支路和参数误差的方法。首先建立了含断路器的支路模型,确定了利用断路器状态估计的误差方程。然后,提出了一种基于扩展状态估计方程的能够同时辨识网络拓扑误差、参数误差和坏数据量测的多源误差的方法。所提拓扑结构误差辨识方法能够在含有坏数据和网络参数误差下辨识不正常状态支路。该方法将约束条件中的归一化拉格朗日乘子加入到断路器模型和参数误差中,通过估计含有拓扑和参数误差的可疑支路的参数来辨识不正常支路的状态,且仅利用传统状态估计的结果来辨识误差。最后利用IEEE118节点系统进行算例验证,对比分析了多种场景下所提方法与传统辨识方法的结果,说明了所提方法的有效性。     

基于自适应容积卡尔曼滤波算法的电力系统动态谐波状态估计

由于传统的谐波状态估计的参数辨识算法要求噪声的协方差矩阵固定不变,而实际工程中噪声的协方差矩阵是随时间变化的,工程中存在错误的量测数据,导致传统参数辨识算法估计的谐波电流参数的准确度较低。因此,提出自适应容积卡尔曼滤波算法来提高辨识谐波电流参数的准确度。首先,针对时变噪声干扰,采用基于渐消记忆指数加权法的噪声估值器算法生成时变噪声的协方差矩阵;其次,针对错误的量测数据,采用开窗估计算法修正错误的量测数据;然后,将修正的噪声协方差矩阵和量测数据代入容积卡尔曼滤波算法中,对谐波电流参数进行估计;最后,搭建IEEE 13节点系统仿真模型,验证了自适应容积卡尔曼滤波算法在时变噪声干扰及量测数据错误情况下仍可准确地估计谐波电流参数,确保了动态谐波状态估计的准确性。     

Outlier accommodation in moving-horizon state estimation: A risk-averse performance-specified approach

This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman-Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer-specified threshold. When measurements with outliers are used (ie, missed detections), both the state estimate and the error covariance matrix become corrupted. This corrupted state and covariance estimate are then the basis for all subsequent outlier decisions. When valid measurements are rejected (ie, false alarms), potentially using the corrupted state estimate and error covariance, measurement information is lost. Either using invalid information or discarding too much valid information can result in divergence of the KF. An alternative approach is moving-horizon (MH) state estimation, which maintains all recent measurement data within a moving window with a time horizon of length L. In MH approaches, the number of measurements available for state estimation is affected by both the number of measurements per time step and the number of time steps L over which measurements are retained. Risk-averse performance-specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This paper derives and formulates the MH-RAPS solution for outlier accommodation. The paper also presents implementation results. The MH-RAPS application uses Global Navigation Satellite Systems measurements to estimate the state of a moving platform using a position, velocity, and acceleration model. In this application, MH-RAPS performance is compared with MH-NP state estimation.     

Topology error identification using normalized Lagrange multipliers

This paper introduces a method for topology error identification based on the use of normalized Lagrange multipliers. The proposed methodology models circuit breakers as network switching branches whose statuses are treated as operational constraints in the state estimation problem. The corresponding Lagrange multipliers are then normalized and used as a tool for topology error identification, in the same fashion as measurement normalized residuals are conventionally employed for analog bad data processing. Results of tests performed with the proposed algorithm for different types of topology errors are reported     

A recursive measurement error estimation identification method forbad data analysis in power system state estimation

A recursive measurement error estimation identification algorithm is proposed for identifying multiple interacting bad data in power system static state estimation. A set of linearized formulae are developed and used to recursively calculate normalized residuals and normalized measurement error estimates upon which the bad data identification method is based. Sparse vector and partial factor modification techniques are used in the recursive identification calculations. Neither the submatrix of the residual sensitivity matrix, W_ss, nor state reestimation is needed in the whole identification process. Digital tests on various power systems, including a 171 bus real system, are done to show the validity and efficiency of the proposed bad data identification method     

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。     

利用贝叶斯理论对电工产品进行可靠性评估

一个混合威布尔分布表示一个总体由若干个威布尔子总体组成。通过联合运用最小二乘法、贝叶斯定理和对单一威布尔分布的参数估计法,对服从混合威布尔分布的电工产品平均寿命及可靠度进行了估计,并结合实际试验数据进行了计算。结果表明,利用该方法能得到较精确的估计。     

一种垂直一体化电网静态参数修正方法

模型管理系统中时常存在新投入设备的参数输入错误和临近退役设备的参数漂移或突变的现象,这使得依赖于基础数据的各项高级应用计算的可信度降低,针对这一问题,提出了一种基于智能调度平台的垂直一体化参数辨识修正方法。利用局部参数错误导致状态估计的量测残差会出现局部偏大的现象,定义可疑量测评价函数,建立基于可疑量测评价函数的参数综合可疑度指标,将可疑度较大的参数作为可疑参数的初始集。进而针对设备的全生命周期标志,将可能存在参数错误的新投入设备和临退役设备的参数并入可疑参数集中。排除部分影响很小的可疑参数后得到最终的候选可疑参数集,调度中心模型管理系统对候选可疑参数进行维护之后通告所辖各级用户（子调度）对模型参数数据库进行更新。采用IEEE 14节点系统验证了方法的有效性。     

基于最大测点正常率的线路参数增广状态估计方法

线路参数误差会导致状态估计不准确,进而影响能量管理系统的高级应用。基于加权最小二乘的增广状态估计法是一种有代表性的线路参数估计方法,但该方法存在易受量测误差影响等问题。基于测量不确定度理论,提出了一种以最大测点正常率(MNMR)为目标函数的线路参数增广状态估计新方法。与传统增广状态估计方法不同,该方法基于测量不确定度信息,目标是使测点的正常率最大,同时考虑电力系统实际的潮流和物理约束信息。此外,采用高斯核密度估计和点估计方法来提取参数估计结果的统计特征。仿真实验表明,所述方法继承了MNMR抗差状态估计的"抗差"特性,辨识结果不易受量测误差影响,能得到更加合理的线路参数。     

两级线性迭代策略下的混合状态估计的研究

基于卡尔曼滤波及线性迭代基本原理,针对当前电力系统混合状态估计精度低、滤波效果差及收敛能力低等问题,提出了一种基于两级线性迭代的电力系统混合状态估计的研究策略:第1级利用相量测量单元(PMU)的量测数据进行线性估计;第2级将其与传统量测值相结合用于状态估计,并利用PMU的高频特性对两级的量测数据进行多次迭代采样。将其在IEEE 14和IEEE 57节点测试系统进行测试,并将结果与其他混合模型比较,结果表明,该策略的估计精度、数据收敛度及量测参数误差均优于其他混合模型。     

Multiphase transmission line modeling for voltage sag estimation

This paper presents a novel approach for voltage sag indices calculation based on instantaneous voltage estimation. The estimation uses traditional state estimation where redundant measurements are available. The estimation is based on time domain state estimation which uses time domain modeling of the power network. The time domain current monitoring is used to have linear mapping and to achieve high performance of voltage sag estimation. The fault estimation procedure is prior of the voltage sag estimation. This paper shows a possible for fault instance detection, fault location identification and fault type estimation method that are required to estimate voltage sag for different line models utilizing residual analysis and topology error processing. Lumped parameter and distributed parameter transmission line modeling are developed to estimate instantaneous voltage at a three-phase power system in time domain. Magnitude and duration of voltage sag as main indices are calculated from the estimated instantaneous bus voltage. The performance of the novel approach is tested on IEEE 14 bus system and the results are shown.     

计及PMU的混合非线性状态估计新方法

提出一种将同步相量测量单元（PMU）的直接电压相量量测变换为间接支路电流量测,并与监控与数据采集（SCADA）量测量一起进行混合迭代的非线性状态估计方法。对构造等效电流修正量而带来的间接量测误差进行了详细和定量的分析,并对该混合估计算法的精度进行了定性分析和定量计算。理论分析和算例表明,该方法可以获得较高的估计精度,在收敛次数和滤波效果上也有所改进。