一种新的快速解耦配电网状态估计方法

本文提出了一种新的快速耦配电网状态估计算法。算法采用加权最小二乘估计模型。在等效电流法的基础上引入旋转操作从而使雅可比国和信息矩阵解耦。因为雅可比矩阵完全解耦，所以信息矩阵的解耦是严格的。仿真算例表明该方法在收敛速度和估计精度上均优于等效电流法。     

Estimation of noise covariance matrices for periodic systems

Estimation of the noise covariance matrices for linear time‐variant stochastic dynamic periodic systems is treated. The novel offline method for estimation of the covariance matrices of the state and measurement noises is designed. The method is based on analysis of second‐order statistics of the state estimate produced by the linear multi‐step predictor. The estimates of the noise covariance matrices are unbiased and converge to the true values with increasing number of data. The theoretical results are illustrated in numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.     

Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo

Linear time‐invariant systems play significant role in the control field. A number of methods have been published for identification of the deterministic part of a process. However, identification of the stochastic part has had much less attention. This paper deals with estimation of covariance matrices of the noise entering a linear system. The process and measurement noise covariance matrices are tuning parameters of the Kalman filter, and they affect the quality of the state estimation. The noise covariance matrices are generally not known, and their estimation from the measured data is a challenging task. This paper introduces a method for estimation of the noise covariance matrices using Bayesian approach along with Monte Carlo numerical methods. Performance of the approach is tested on various systems and noise properties. The second part of the paper compares Monte Carlo approach with the recently published methods. The speed of convergence is compared with the Cramér–Rao bounds. Copyright © 2012 John Wiley & Sons, Ltd.     

A convex optimization approach to adaptive stabilization of discrete‐time LTI systems with polytopic uncertainties

This paper suggests a simple convex optimization approach to state‐feedback adaptive stabilization problem for a class of discrete‐time LTI systems subject to polytopic uncertainties. The proposed method relies on estimating the uncertain parameters by solving an online optimization at each time step, such as a linear or quadratic programming, and then, on tuning the control law with that information, which can be conceptually viewed as a kind of gain‐scheduling or indirect adaptive control. Specifically, an admissible domain of stabilizing state‐feedback gain matrices is designed offline by means of linear matrix inequality problems, and based on the online estimation of the uncertain parameters, the state‐feedback gain matrix is calculated over the set of stabilizing feedback gains. The proposed stabilization algorithm guarantees the asymptotic stability of the overall closed‐loop control system. An example is given to show the effectiveness of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems

A decentralized unscented Kalman filter (UKF) method based on a consensus algorithm for multi-area power system dynamic state estimation is presented in this paper. The overall system is split into a certain number of non-overlapping areas. Firstly, each area executes its own dynamic state estimation based on local measurements by using the UKF. Next, the consensus algorithm is required to perform only local communications between neighboring areas to diffuse local state information. Finally, according to the global state information obtained by the consensus algorithm, the UKF is run again for each area. Its performance is compared with the distributed UKF without consensus algorithm on the IEEE 14-bus and 118-bus systems. The low communication requirements and high estimation accuracy of the decentralized UKF make it an alternative solution to the multi-area power system dynamic state estimation.     

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。     

Integral state estimation for well-conditioned and ill-conditioned power systems

This paper describes a generalized integral solution method to solve the power system state estimation problems for well-conditioned and ill-conditioned power systems. By simple graph analysis, the condition number, which is derived from the simple graph analysis, can be used for justification and determination of various power system operating situations. The modified fast decoupled state estimation method for well-conditioned power systems and the Crout decomposition method for ill-conditioned power systems are formulated and discussed in this paper. Simulations and results are discussed to support the feasibility and effectiveness of the proposed methods for well- and ill-conditioned power system state estimation.     

A robust method for equality constrained state estimation

This paper presents a robust method for the solution of the equality constrained state estimation problem. The problem is formulated as a weighted least squares (WLS) problem with equality constraints. Traditionally the problem is solved iteratively by the normal equations with equality constraints (NE/C). One problem of NE/C is that the gain matrices are not positive definite and extra logic is required to process the zero pivots during Gaussian elimination. In this. paper, positive definite gain matrices are formulated and triangularization is performed symbolically using sparsity techniques. The method is illustrated with a six-bus system and the IEEE 14-bus system     

极坐标系下的快速等效电流量测变换状态估计方法

利用SCADA系统提供的多种类型量测数据,推导出极坐标系下的等效电流量测变换算法。为了提高计算速度,采用了快速解耦法的两个合理假设条件,将雅可比矩阵简化为常数矩阵,并根据该矩阵的特点对信息矩阵进行分块化简,最终得到解耦的信息矩阵,将信息矩阵规模最终减少到常规算法的1／4。此外,还提出了一种直接生成信息矩阵的方法。理论分析与WSCC和NewEngland两个系统的算例表明该方法具有简单、快速、线性收敛的特点,能够满足大型电力系统实时状态估计的需要。     

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single‐input–single‐output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple‐input–multiple‐output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method.     

基于改进UKF滤波的永磁同步电动机矢量控制

针对普通UKF在永磁同步电动机速度估计中存在对模型不确定性的鲁棒性差、对突变状态的跟踪能力低和收敛速度慢等问题,结合强跟踪滤波器对UKF滤波进行改进,引入时变渐消因子在线自适应调整增益矩阵和状态预测误差协方差矩阵,实现残差序列正交或近似正交,强迫UKF滤波保持对实际状态的快速跟踪.将该算法在永磁同步电动机无速度传感器矢量控制系统中进行仿真研究.试验结果与统计分析表明,相对与普通UKF,基于改进UKF滤波的永磁同步电动机转子速度及角度估计更准确,误差更小,跟踪速度更快,鲁棒性更好.     

矩阵二阶系统的H∞观测器设计--一种参数化方法

利用一种完全参数化的设计方法,直接在矩阵二阶系统的框架下研究不确定性矩阵二阶系统的H∞观测器设计问题,目标是使观测过程在保持鲁棒稳定并达到期望的动态特性的同时,还能使得外部干扰到误差状态的传递函数的H∞范数小于事先给定的上界数值。基于矩阵二阶系统的Sylvester方程的完全参数化解,通过一组设计参数建立了观测器的所有增益矩阵以及观测器系统矩阵的左特征向量的完全参数化形式。并在此基础上,将H∞范数的上界约束条件转化成一个等价的约束设计参数的条件。该方法能够提供所有的设计自由度,有很强的应用价值。最后通过一个弹簧质量系统表明此方法的有效性。     

An adaptive control scheme using multiple reference models

This paper develops an extended model reference adaptive control scheme to expand the capacity of state feedback state tracking adaptive control to handle the plant‐model matching uncertainties for single‐input LTI systems. The extended scheme is developed, using multiple reference model systems (only one of which is required to be able to match the controlled plant), and multiple controllers (which are updated from adaptive laws generated from multiple reference model systems based estimation errors), as two key features of such design to relax a plant‐model matching condition. A switching mechanism is constructed using those multiple estimation errors, capable of selecting the suitable control input from the multiple control signals, to achieve the desired system performance. An aircraft flight control example is presented to show the capacity of such design in relaxing a practical design condition. Copyright © 2013 John Wiley & Sons, Ltd.     

基于改进Att-LSTNet与无迹粒子滤波融合的主动配电网预测辅助状态估计

针对传统的无迹粒子滤波(unscented particle filter, UPF)存在不准确的新息向量及未知的量测噪声协方差矩阵导致估计精度低的问题,提出一种改进Att-LSTNet与UPF融合的主动配电网预测辅助状态估计(forecasting-aided state estimation, FASE)方法。首先,采用引力搜索算法(gravitational search algorithm, GSA)对支持向量回归(support vector regression, SVR)的关键参数进行优化处理,利用历史数据建立GSA-SVR模型,并将其引入至Att-LSTNet模型的输出层,构建一种增强预测模型。然后,利用UPF中的新息向量来训练该模型,并结合孤立森林算法和箱线图法对原始新息向量进行监控和修正。最后,针对量测噪声协方差矩阵未知的情况,结合修正后的新息向量和UPF计算出未知量测噪声协方差矩阵,并进行状态估计。基于IEEE33与IEEE118节点标准配电系统的算例结果表明,所提出的方法在估计精度、泛化能力和鲁棒性等方面具有优越性。     

一种改进的Hachtel状态估计方法

电力系统状态估计常用加权最小二乘(W LS)法处理,这种方法中量测权值的悬殊和大量的注入量测会导致信息矩阵出现病态问题,降低算法的收敛性。综合带约束的正规方程(NE/C)法和海克特(H ach te l)法数值稳定性好的优点,把量测量合理分类构建信息矩阵,并采用分块稀疏矩阵技术,形成了一种计算速度快、数值稳定性好的状态估计新算法。理论和算例分析验证了该算法的有效性。     

Measurement placement for power system state estimation using decomposition technique

This paper applies the concept of distributed processing to the problem of measurement placement for power system state estimation. The proposed method uses the minimum condition number of the measurement matrix as a criterion in conjunction with sequential elimination to reach the near optimal placement positions. Firstly, the entire network of the power system is decomposed into smaller sub-networks. Then, in each sub-network, the optimal positions for measurement placement are determined by using the minimum condition number criteria. A heuristic algorithm for reducing the number of placement sites is also presented in order to minimize the communication costs. The numerical experimental results on the IEEE 14, 30, and 118 bus systems indicate that the proposed technique will provide a measurement matrix with smaller condition number and the computation time is much shorter than the non-decomposition approach.     

Process noise covariance estimation via stochastic approximation

Kalman filtering for linear systems is known to provide the minimum variance estimation error, under the assumption that the model dynamics is known. While many system identification tools are available for computing the system matrices from experimental data, estimating the statistics of the output and process noises is still an open problem. Correlation-based approaches are very fast and sufficiently accurate, but there are typically restrictions on the number of noise covariance elements that can be estimated. On the other hand, maximum likelihood methods estimate all elements with high accuracy, but they are computationally expensive, and they require the use of external optimization solvers. In this paper, we propose an alternative solution, tailored for process noise covariance estimation and based on stochastic approximation and gradient-free optimization, that provides a good trade-off in terms of performance and computational load, and is also easy to implement. The effectiveness of the method as compared to the state of the art is shown on a number of recently proposed benchmark examples.     

电力系统中厂站/全网双层状态估计

分析了分层状态估计的研究现状,提出了厂站／全网双层式状态估算法,还提出了降价组合估计辨识法的新算法和残差加权估计的新概念,将石站估计与全网估计很好地予以协调。该算法较好地解决了多相相关不良数据的辨识问题,提高了工种实践中状态估计软件运行的鲁棒性。     

An improved sequential method for AC/MTDC power system stateestimation

This paper reports the development of a novel and effective approach in AC/MTDC (multi-terminal DC) power system state estimation, called improved sequential method. The proposed approach is sequential in nature in which the MTDC system is solved followed by the AC system. Because the new method decouples the AC and MTDC systems without neglecting the coupling submatrices in the gain matrix, it exhibits good convergence characteristics. The variables and measurement equations of the MTDC system related to the problem formulation are discussed. The effectiveness of the proposed algorithm is demonstrated in this paper with extensive testing in several test systems and the results are compared with other state estimators. The main advantage of the method is that the proposed algorithm ran be used to easily adapt existing state estimation algorithm to cater for MTDC     

An Optimal PMU Placement Method Against Measurement Loss and Branch Outage

This paper presents a new method for an optimal measurement placement of phasor measurement units (PMUs) for power system state estimation. The proposed method considers two types of contingency conditions (i.e., single measurement loss and single-branch outage) in order to obtain a reliable measurement system. First, the minimum condition number of the normalized measurement matrix is used as the criteria in conjunction with the sequential elimination approach to obtain a completely determined condition. Next, a sequential addition approach is used to search for necessary candidates for single measurement loss and single-branch outage conditions. These redundant measurements are optimized by binary integer programming. Finally, in order to minimize the number of PMU placement sites, a heuristic technique to rearrange measurement positions is also proposed. Numerical results on the IEEE test systems are demonstrated