A reduced model for bad data processing in state estimation

A reduced model theory for bad data processing is proposed which utilizes the concept of error residual spread areas. Based on the reduced model theory, statistical indices can be defined for each error residual spread area, and therefore existing detection and identification techniques can be applied separately for each error residual spread area. In this way, errors can be isolated in smaller regions of the system, making it possible to avoid the search for bad data in the global system. Results from several test cases on power systems show the effectiveness and robustness of the method     

Fast identification of bad data in power system tracking state estimation

‘Physical’ removal of suspect bad data and re-estimation during identification requires recalculation of gain matrices and re-ordering of lists that graph them, and is thus time-consuming. A ‘mathematical’ removal technique is presented which avoids re-ordering and retriangularisation of gain matrices but nevertheless delivers precisely the same state estimates. Numerical tests reveal no degradation in convergence when mathematical removal is used. Pseudomeasurement replacement of bad data is advocated when the measurement set, after removal, becomes unobservable. Test results indicate that replacement by either the previous measurement or an estimate based on previous scan states works well provided the pseudomeasurements are de-emphasised by increasing their variances whenever the changes in power system states between measurement scans are much greater than 0.001 p.u.     

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 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     

基于支持向量机的电力系统状态估计 多类型数据异常检测

为了解决异常数据严重影响电力系统状态估计性能的问题,提出了一种基于支持向量机(SVM) 的电力系统预测辅助 状态估计(FASE) 多类型数据异常检测方法。首先,针对传统 FASE 的预测准确率欠佳的问题,提出了基于极限学习机的 FASE 方法,并利用SVM 并基于预测数据、量测数据与估计值,实现了对坏数据、负荷突变和单相接地等多种类型的数据异常 检测。其次,针对惩罚因子和核函数参数会影响分类精度的问题,提出采用灰狼算法对 SVM 参数进行优化,在兼顾计算速度 的同时提高了数据异常检测的准确率。最后,在IEEE33 和丹麦DTU7K47 节点主动配电网系统上进行仿真测试,所提方法 在正常工况下提升26.08%与26.76%,计算速度提升46.05%,在数据异常情况下准确率综合提升32.04%与29.27%,结果 表明,所提方法具备较强的通用性与实时性,可以有效地检测电力系统中各种类型的数据异常,并提高状态估计的性能。     

Variance analysis of robust state estimation in power system using influence function

An analytical equation is derived using influence function approximation to calculate the variance of the state estimate for traditional robust state estimators such as the Quadratic-Constant, Quadratic-Linear, Square-Root, Schweppe-Huber Generalized-M and Multiple-Segment estimator. The equation gives insights into the precision of the estimation. Using the equation, the variance of a state estimate can be expressed as a function of measurement noise variances enabling the selection of sensors for a specified estimator precision. It can also be used to search for the optimum estimator parameters to give the minimum sum of variances. The well-known Weighted-Least-Squares variance formula is a special case of the equation and simulations on the IEEE 14-bus system are given to show the usefulness of the equation.     

基于非线性状态估计的虚假数据注入攻击代价分析

随着智能电网的发展,信息通信系统与物理电力系统深度融合,虚假数据注入等网络攻击可能会对电网的安全稳定造成严重影响,目前这方面研究已成热点问题。一次成功的虚假数据注入攻击涉及攻击者所掌握资源、攻击区域选择和攻击向量构建。在有限的资源下,根据实际电网运行特征,以攻击节点为中心,构建了单节点攻击区域和多节点攻击区域,一定程度上可缩小攻击范围。基于非线性状态估计模型,分别针对单节点攻击与多节点攻击情形,提出一种掌握局部电网信息下的攻击代价分析方法。最后以IEEE-14系统和IEEE-1354系统为例,分别进行单节点攻击和多节点攻击分析,其结果验证了所提虚假数据注入攻击代价分析方法的有效性。     

Suspected bad data substitution using a search emulation procedure

The ability to study and operate large power systems has been significantly increased by telemetering more and more remote data to a central control center. The effectiveness of computational and operational activities depends directly on the ability to detect, identify and substitute for bad data being sent from the remote stations. This substitution process is often done by a person tracking flows back through the system and estimating values for suspected bad data from other data inputs. This paper presents a computer procedure which emulates the tracking process done by the human estimator and automatically verifies suspect data.     

Exact-decoupled rectangular-coordinates state estimation withefficient data structure management

The authors deal with the solution of power system estimate problems using node voltages in rectangular coordinates. The Jacobian matrix is diagonalized, and the mismatching vector is modified following the ideas proposed by other investigators for decoupling the Jacobian matrix when using polar coordinates. The proposed method decouples the Jacobian matrix into real and reactive-power submatrices, which are evaluated only once at the beginning of the process. An efficient data structure management technique is presented to improve the computational process. The performances of these techniques are evaluated using several power system networks. The proposed rectangular-coordinate technique is compared with the Newton-Raphson method and a polar coordinate method. The proposed data-storing method is compared with a standard technique     

State estimation and bad data processing for systems including PMU and SCADA measurements

Conventional state estimators (SE) are based on real-time measurements, consisting of bus voltages and active and reactive power flows and injections, and estimate the voltage phasors of the network buses. Until recently, these measurements were obtained only through SCADA. With the advent of GPS synchronized measurements obtained by phasor measurement units (PMU), effective techniques are required to incorporate the extremely accurate PMU measurements into state estimation, in order to improve its performance and observability. This paper develops a non-linear weighted least squares estimator by modeling the current phasor measurements either in rectangular or in polar coordinates and compares the two approaches. Any numerical problems arised at flat start or for lightly loaded lines, are resolved. The error amplification, due to the current phasor measurement transformation from polar into rectangular coordinates, is also investigated. The normalized residual test is used to effectively identify any bad data in the conventional and phasor measurements. The proposed techniques are tested with the IEEE 14-bus system.     

Distribution circuit state estimation using a probabilisticapproach

Past work on distribution circuit state estimation has focused on the adoption of a transmission state estimator approach, without necessarily accounting for the specific requirement of a distribution circuit-based analysis. On distribution circuits, typically, there are very few available real-time measurements, and thus, researchers have treated customer load demand estimates as pseudo-measurements in a weighted-least-squares formulation. This can lead to convergence problems and also, the approach effectively assumes that all bus load demands are normally distributed (Gaussian) which may not be valid on distribution circuits. This paper presents an alternative approach to distribution circuit state estimation using a probabilistic extension of the radial load flow algorithm while accounting for real-time measurements as solution constraint. The algorithm which takes advantage of the radial nature of distribution circuits also accounts for other issues specific to distribution circuits. Namely, the algorithm accounts for nonnormally distributed loads, incorporates the concept of load diversity (load correlation) and can interact with a load allocation routine. The effectiveness of the algorithm is illustrated through comparisons made with Monte Carlo simulations     

Distribution system state estimation using compressive sensing

This paper leverages the big data provided by micro-phasor measurement units (μPMUs) placed along the smart distribution networks for distribution system state estimation (DSSE). We propose a novel and straightforward DSSE algorithm by solving a set of linear equations without any iterative process. The μPMUs are placed at few buses to measure the voltage phasors. The measured voltage vector is expressed as the product of current injections vector and impedance matrix of the system. Since number of μPMUs is less than number of buses, the constructed set of linear equations are underdetermined. Furthermore, the injection currents vector is sparse because any single load/generator current is negligible when compared with the total current injected from the external grid to the distribution network. Subsequently, we use Compressive Sensing and ℓ¹-norm minimization to recover the sparse current vector from the limited number of μPMUs. The voltages at all buses are obtained by multiplying the reconstructed current vector by the impedance matrix. Performance of our method is demonstrated on the IEEE 123-bus test system and a 13.8-kV, 134-bus real network with different distributed generations (DGs) penetration level and under a weakly meshed operation mode. Also, the performance of the proposed technique is compared with that of the conventional weighted least-square (WLS) method.     

Hierarchical state estimation using a fast rectangular-coordinatemethod [power system analysis computing]

A hierarchical computing scheme for power system state estimation is developed by carefully considering mismatches arising from the system decomposition. The method makes use of an extension of a fast second-order load-flow method that allows a fixed Jacobian matrix to be used in the hierarchical algorithm. In the problem formulation, the power network is decomposed into two or more subsystems; the interaction among them is taken into account through the tie-line bus voltages. The hierarchical structure of the method consists of two levels: the upper level, where the optimal tie-line bus voltages are evaluated; and the lower level, where the optimal states of each subsystem are determined by minimizing a cost function that involves the entire system. Three test cases using the method are reported here and the test results are compared with those obtained by another hierarchical power system state estimation method     

基于智能电能表数据的配电网状态估计

含有可再生能源与需求响应资源(demand response resource,DRR)的配电网系统需要精确的状态估计,以进行实时控制和调度。配电网状态估计通常依赖于智能电表的测量,但测量设备的增加不仅对通信成本要求高,同时还会导致控制问题的高复杂性。针对这些问题,提出一种低压配电网所需智能电表实时数据的分析方法及其在状态估计中的应用。同时讨论了测量值组合的作用,以减轻状态估计问题中由于大量数据产生的计算压力。通过实验验证了方法的有效性。     

Decoupled internal state estimation using external system equivalents

An algorithm for internal state estimation of an interconnected power system is presented using three truly decoupled external system equivalent models. The external system models are identified from the real-time internal measurements being processed by the Kalman filtering technique. These identified models are placed on the boundaries of the internal system so as to reflect the reaction of the external system arising in response to the internal system conditions. The resulting modified internal system model is then utilized to develop a weighted-least-square (WLS) version of the fast decoupled internal state estimation algorithm. The algorithm has been tested on a 25-bus test system and similar convergence characteristics are obtained for all three types of external system models. Moreover, it is found to take the same number of iterations as that of an overall estimator with a drastic reduction in its requirements of computer time and storage.     

Multi area state estimation using synchronized phasor measurements

This paper investigates the problem of state estimation in very large power systems, which may contain several control areas. An estimation approach which coordinates locally obtained decentralized estimates while improving bad data processing capability at the area boundaries is presented. Each area is held responsible for maintaining a sufficiently redundant measurement set to allow bad data processing among its internal measurements. It is assumed that synchronized phasor measurements from different area buses are available in addition to the conventional measurements provided by the substation remote terminal units. The estimator is implemented and tested using different measurement configurations for the IEEE 118-bus test system and the 4520-bus ERCOT system.