A new numerical algorithm to check the observability and bad-data processing ability of the metering scheme

This paper provides a unified approach for the optimization of measurements placements employed for power system online monitoring through state estimation. The proposed methodology, which can be suitable for the mixed measure system, preserves state estimation observability and bad-data processing capability by employing numerical algorithms for observability checking, critical measurements and critical couple identification. First, node injection radix measurements and measurement categories are defined. According to the above definitions, the coefficient matrix can be solved. The analysis on the column vectors of the coefficient matrix can determine each measurement classification. Furthermore, the numbers of each measurement class contains can determine bad-data processing capability. The observability can be checked by the type number of measurements. The proposed method is illustrated with the IEEE39-bus system and the IEEE118-bus system. Results from the case studies are presented to demonstrate that the approach adequately fulfills the desired properties related to observability, bad-data processing, cost, and robustness.     

Observability analysis and bad data processing for state estimationwith equality constraints

A factorization-based observability analysis and the normalized residual-based bad-data processing have been carried out for state estimation using the normal equation approach. The observability analysis is conducted during the process of triangular factorization of the gain matrix. The normalized residuals are calculated using the sparse inverse of the gain matrix. The method of Lagrange multipliers is applied to handle state estimation with equality constraints arising from zero injections, because of its better numerical robustness. The method uses a different coefficient matrix in place of the gain matrix at each iteration. The factorization-based observability analysis and normalized residual-based bad-data processing are extended to state estimation with equality constraints. It is shown that the observability analysis can be carried out in the triangular factorization of the coefficient matrix, and the normalized residuals can be calculated using the sparse inverse of this matrix. Test results are presented     

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     

Robust dynamic state estimation of power systems based onM-Estimation and realistic modeling of system dynamics

This paper presents a new scheme of dynamic state estimation, utilizing a statistical approach called the M-Estimation to resolve the filtering problem robustly. In the prediction step, realistic treatment of system dynamics based on nodal analysis produces an efficient state prediction method. The proposed Robust Realistic Dynamic State Estimation (RRDSE) has been tested on 5-bus, 14-bus, and 30-bus test systems and the results are presented. The error analysis presented reveals the superiority of the proposed RRDSE particularly when the system measurements are under bad-data condition     

基于增广状态估计的混合不良数据诊断与参数辨识

电网同时存在遥测坏数据和参数错误时,由于坏数据会影响参数辨识结果,全网参数辨识和估计方法很难保证结果的准确性。文中提出一种基于增广状态估计的混合不良数据诊断与参数辨识方法,先通过残差平衡度判断不良数据是遥测坏数据还是错误参数,将遥测坏数据直接剔除;然后,通过分区方法将多个潜在的不良参数尽可能分开在不同的局部区域,以减弱不良数据之间的相互影响;最后,采用分区增广状态估计方法修正不良参数。算例结果表明,该方法能有效区分坏数据和错误参数,且分区参数辨识能避免不良数据之间相互影响,从而提高了可疑参数辨识的精度。     

改进迭代自组织数据分析法的不良数据辨识

针对迭代自组织数据分析技术A(iterative selforgani zing data analysis technique A-ISODATlA)对初始聚类中心敏感问题,提出将基于遗传算法的改进ISODATA方法用于电力系统不良数据辨识。该方法采用量测值的标准残差rN 和相邻采样时刻量测值之差△z作为特征值,然后采用遗传算法形成初始分类矩阵,通过遗传操作进行迭代计算,实现量测数据的模糊聚类分析,最终根据隶属度的大小来辨识其是否属于不良数据。算例表明,该方法能最终获得全局最优解,从而进行准确的不良数据辨识,避免了初始聚类中心对分类结果的影响。     

Sequential bad data analysis in state estimation using orthogonaltransformations

The sequential identification of multiple bad data in power system state estimation using orthogonal transformations is described. The method involves iteratively building a list of suspect bad data based on their normalized residuals. The measurements are then analyzed for their estimated errors, and the suspect list is pruned to reveal the bad data. Valid measurements are then returned to the system for completing the solution. As part of this development, a new method of computing and updating the residual covariance matrix is also presented. Test results on the IEEE 30-bus system are presented     

An integer-arithmetic algorithm for observability analysis of systems with SCADA and PMU measurements

This paper presents an efficient numerical method for observability analysis of systems including both conventional (SCADA) measurements and synchronized phasor (PMU) measurements, using integer preserving Gaussian elimination of integer coefficient matrices. The observable islands are identified in a noniterative manner, by performing backward substitutions on the integer triangular factors of the integer gain matrix. Multiple placement of conventional and phasor measurements for a system that is found to be unobservable is done by a direct method, using the integer triangular factors of a Gram matrix associated with a reduced size Jacobian matrix. Since all computations performed are exact, no round-off error, numerical instability, or zero identification problems occur. The IEEE 14-bus system is used to illustrate the steps of the proposed method. Test results for the IEEE 300-bus and the FRCC 3949-bus systems are provided to demonstrate the features of the proposed algorithms.     

Development of a new algorithm for power flow analysis

This paper presents a new iterative solution technique for power flow analysis to reduce the computation complexity, hence time of the conventional solution techniques. In the proposed method, the impedance matrix has been used instead of admittance matrix. This method does not involve any other jacobian matrix or any other inversion of matrix, hence there is no problem of singularity. Memory requirement of the proposed method is also less. The new method has been tested on IEEE standard 5-bus, 14-bus, 30-bus, 57-bus, 118-bus and 300-bus test systems with high precession. The test results have been compared with the same of popular conventional solution methods. The method has also been tested under different practical security constraints. The test results presented reveal the superiority of the proposed method.     

Implementation of the hypothesis testing identification in powersystem state estimation

The authors consider the online implementation of a general, reliable and efficient bad-data analysis procedure for power system state estimation. It is based on hypothesis testing identification, which was previously proposed and subsequently improved by the authors. The procedure involves a sequential measurement error estimator along with adequate sparsity programming techniques. Both make the procedure easy to implement on any state estimator. A criterion for multiple noninteracting bad-data identification is also proposed, which is applicable to any bad-data analysis method. Simulations are reported on systems of up to 700 buses. A thorough comparison with classical methods is also included     

Allocation of transmission network cost using modified Zbus matrix

This paper presents a new method to allocate the transmission cost among generators and loads. The allocation is calculated for each transmission line through the identification and quantification of a fair distribution of both generators and loads. This method is based on circuit theory using modified impedance matrix. A two-step scheme is performed here: (1) in order to determine the participation of generators in the costs of the transmission network the generators are modeled as current sources and loads as constant impedances. (2) to determine the contributions of loads in the cost of the transmission network the loads are modeled as current sources and generators as constant impedances. Numerical results with a four-bus test system, as well as with IEEE 24-bus test system are reported and discussed to demonstrate the applicability of the proposed method.     

稀疏技术在电力系统状态估计中的应用

研究了电力系统状态估计中稀疏技术的应用方法。首先根据因子矩阵和消去树，讨论了稀疏向量法。然后基于给定的稀疏矩阵存储方法和符号因子化技术，提出了一种采用稀疏向量法进行LDLT分解的算法，最后结合量测残差方差计算给出了两种应用稀疏技术计算的方法。算法的有效性在IEEE 118和IEEE 300系统上得到了验证。     

基于模糊聚类分析的分层估计算法在DTS中的应用

为了建立调度员培训仿真(DTS)系统逼真的培训初始条件，需要获取EMS／SCADA系统状态估计的结果。文中分析了状态估计的研究现状和模糊聚类分析法在辨识不良数据方面的应用，提出了基于模糊聚类分析的分层估计算法，即先在厂站级对量测数据进行检测和模糊聚类辨识，根据隶属度的大小辨识出不良数据和对不良数据进行重新估计，再进行全网状态估计。该算法将厂站估计与全网估计加以协调，较好地解决了多相关不良数据的辨识及整体式状态估计中的残差污染问题，并成功应用于河南电网DTS系统中，提供了良好的数据基础。     

Autoregressive Filters for the Identification and Replacement of Bad Data in Power System State Estimation

The quadratic cost function J(x?) and the normalized residuals rN are used conventionally for identifying the presence and location of bad measurements in power system state estimation. These are "post estimation" tests and therefore require the complete re-estimation of system states whenever bad data is identified. This paper presents a pre-estimation filter for detection and identification of gross measurement errors. The basic function of this filter is to compare the measured value of a system variable with its predicted value obtained using an autoregressive (AR) model. If the difference exceeds a pre-determined threshold, the measured value is discarded in favor of the predicted value. Each measurement is processed by an AR filter before being used in the state estimation. The performance of the AR filter is tested against that of the J(x?) and rN tests and the results are reported in this paper. The principal advantage of the AR filtering scheme is its speed in bad data identification. Furthermore, it can be used to complement other bad data processing methods.     

一种不良数据辨识系统的研究与实现

采用VC＋＋与Fortran 6.5语言混合编程,开发了一种基于模糊聚类分析的电力系统不良数据辨识系统。在理论研究的基础上,将基于模糊等价关系和基于模糊等价划分两种模糊聚类方法进行有效综合。通过编程,分别实现了基于等价关系法、基于模糊ISODATA法和基于聚类综合法的不良数据辨识系统,并对比分析了三种模糊聚类方法的特点。算例分析表明,该系统能够快速、准确地辨识出不良数据,并能有效克服残差污染及残差淹没现象,同时具有辨识方法选取灵活、软件界面友好、计算速度快等特点,有良好的应用前景。     

Fuel restricted short term economic dispatch using evolutionary programming for utility system

The objective of the paper is to minimize the production cost of the thermal power generation. An elegant approach is presented in order to obtain the equivalent cost function of the participating non-fuel restricted units and the Economic Dispatch Calculations (EDC) are carried out along with fuel restricted units. The Evolutionary Programming (EP) technique is used for real power optimization with fuel restricted units. The optimal solution is obtained neglecting losses. The Fast Decoupled Load Flow (FDLF) analysis is conducted to find the losses by substituting the generation values. Then the loss is participated among all generating units using participation factor method. The load flow is conducted again and the voltage limit violation is checked. The Algorithm is tested on IEEE 6-bus system IEEE 30-bus system and a 66-bus utility system. The results obtained by this new approach are compared with those obtained using classical method. It is observed that the proposed method is more reliable and efficient.     

Analytical model and algorithm for tracing active power flow based on extended incidence matrix

This paper proposes an analytical model and algorithm for tracing power flow (TPF). The concept, construction approach and properties of extended incidence matrix (EIM) are developed. By using results of an AC or DC power flow solution from any off-line program or state estimation, the extended incidence matrix, generation and load power vectors, and distribution factor matrix are derived so that the analytical model of power transfers between generators and loads can be built. The major advantage of the proposed method is that the matrix theory is used to directly build the TPF model and no proportional sharing assumption on the flow distribution is needed. The method was tested using a 4-bus system, and the IEEE 30-bus and IEEE 14-bus power systems. The case studies indicate that the developed technique can be applied to any power system with or without loop flows.     

Hydrothermal scheduling by augmented Lagrangian: consideration oftransmission constraints and pumped-storage units

This paper presents an augmented Lagrangian (AL) approach to scheduling a generation mix of thermal and hydro resources. AL presents a remedy to duality gap encountered with the ordinary Lagrangian for nonconvex problems. It shapes the Lagrangian function as a hyperparaboloid associating penalty in the direction of the coupling constraints. This work accounts further for the transmission constraints. We use a hydrothermal resource model with pumped-storage units. An IEEE 24-bus test system is used for AL performance illustration. Computational models are all coded in C. The results of the test case show that the AL approach can provide better scheduling results as it can detect optimal on/off schedules of units over a planning horizon at a minimal cost with no constraint violation. It requires no iteration with economic dispatch algorithms. The approach proves accurate and practical for systems with generation diversity and limited transmission capacity     

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。     

基于支路电流的配电网非量测负荷估计

提出一种基于支路电流的抗差估计算法用于修正10kV配电网非量测负荷。该算法将配电网中的功率量测变换为电流量测,实现了雅可比矩阵常数化,支路电流实虚部解耦求解。并采用了权值随残差变化的权函数,较大程度地抑制了伪量测中坏数据对估计结果的不良影响。文中还给出IEEE 33节点配电系统的计算结果。结果表明,本文的算法能够有效地修正伪量测数据,使其准确度达到或接近实际量测值的准确度。