基于等值信息交换的分布式抗差估计算法

提出了一种基于等值信息交换的分布式抗差状态估计算法。各子系统通过等值计算将自身量测信息浓缩成等值信息,协调层收集各子系统的等值信息计算出边界状态量进而实现分布式状态估计。此外,在分布式算法基础上实现了分布式抗差估计。采用等价权原理将指数型目标函数抗差估计方法转换成变权重的加权最小二乘估计,并基于不动点迭代的方法进行求解。在求解过程中,等值信息随着权重值的变化而不断更新,子系统得以综合全系统信息进行抗差估计。最后,构造了多子系统算例和含不良数据的算例对算法进行测试。测试结果表明分布式抗差估计算法具有很高的计算精度和很好的抗差性能。     

变压器分接头位置的估计

本文提出一种新的变压器档位估计方法。它在现有的快速分解状态估计法的基础上,利用增广状态估计法解决了实际运行中变压器档位估计问题。它的有效性已经在实际应用中得到了证明。     

电力系统抗差状态估计研究综述

状态估计是电力系统能量管理系统的重要组成部分.传统加权最小二乘估计不具备抗差性,为此产生了抗差估计理论.本文介绍了抗差估计的定义、目标及基本类型,综述了电力系统抗差状态估计的经典方法和新方法.经典方法主要包括M估计、GM估计、高崩溃污染率估计等;新方法包括新息图状态估计、最大相关熵估计、最小信息损失状态估计、最多赞成状...     

Toward adaptive robust state estimation based on MCC by using the generalized Gaussian density as kernel functions

In this paper, a generic formulation is proposed for robust state estimation (RSE) based on maximum correntropy criterion (MCC), leading to an adaptive robust state estimator. By using the generalized Gaussian density (GGD) as the kernel function, the proposed formulation theoretically unifies several existing RSE models, each of which is optimal for a specific type of measurement noise and error distribution. As the noise and error distribution is generally unknown ex-ante and time-varying in operation, a statistical learning scheme is proposed to heuristically identify the actual distribution type online. Afterwards, the optimal RSE can be properly selected so as to adapt to the variation of noise and error distribution types. Simulations are carried on a rudimentary 2-bus system and the standard IEEE-118 bus system, illustrating the correctness and effectiveness of the proposed methodology.     

新疆电网智能调度支持系统状态估计调试方法研究

针对系统实际运行中的电网状态估计存在的问题,结合电网运行特点进行了分析,对提高状态估计准确率的调试方法的改进进行了研究,并提出相应的实用化调试方法,特别是针对环网线路、多回联络线路、超短线路提出了一种估计参数的方法,在实际工作中验证了其有效性,提高了状态估计的准确性,推动了智能电网调度自动化系统D5000在线分析模块应用实用化的发展。     

基于全网架相量测量的抗差状态估计算法

鉴于海南电网实现了主网架全PMU覆盖,提出了一种基于全PMU量测的抗差状态估计算法。该算法以测点正常率最大为目标,兼顾功率、电压相量和电流相量量测,采用现代内点法实现系统非线性模型的高效求解。由于充分利用所有PMU量测数据,该算法可有效排除不良数据的影响。算例表明,所提算法显著地提升了状态估计的收敛率、合格率和计算频率。     

基于PMU分解协调的状态估计算法

PMU至少可实现系统局部可观,此性质必然产生系统的自动划分,即出现围绕PMU配置点集的若干可观测性子系统。基于这一思路,从状态估计快速性角度出发,提出电网分解协调的状态估计快速算法,分解是指依据PMU配置的可观测子系统的划分,协调是指各子系统如何达到无缝的衔接,在快速估计下达到与整体估计一样的效果。最后,以IEEE-39节点及IEEE-118节点系统对对算法进行了验证。     

一种电力系统量测噪声自适应抗差状态估计方法

为克服传统状态估计方法在处理量测噪声方面的局限性,文中首先提出一种基于最大相关熵准则的抗差状态估计一般模型,该模型可从理论上统一已有的几种抗差状态估计方法,并可导出新的抗差状态估计方法。在此基础上提出一种量测噪声自适应抗差状态估计方法(ARSE),ARSE能够通过统计学习获得量测噪声的分布规律,并与所提出的抗差状态估计一般模型进行在线匹配,从而实现对量测噪声类型的自适应,即在常见的量测噪声分布类型下,ARSE可得到更接近于状态变量真值的估计结果。最后通过仿真算例验证了所述方法的有效性。     

基于混合量测的电力系统状态估计混合算法

研究了相量量测装置(PMU)相量量测和监控与数据采集(SCADA)量测混合使用时的数据匹配问题,提出了利用状态量转换预测得到预报系统状态和预报节点注入电流向量的方法。在此基础上,提出了应用PMU实时相量量测和预报节点注入电流向量的线性静态状态估计算法,以及应用PMU实时相量量测和预报系统状态的线性动态状态估计算法。文中将这2种算法与传统状态估计算法相结合,组成了状态估计混合算法,保证了状态估计的计算精度。该混合算法有效减少了状态估计的计算时间,对PMU的量测配置也没有严格的要求,具有很好的通用性。最后采用IEEE30节点系统对该方法进行了验证。     

广东电网WAMS增强SCADA状态估计研究

SCGM（1,1）模型是灰色预测理论在传统GM（1,1）上的进一步发展。文中首次将单变量的SCGM（1,1）模型应用于中长期电力负荷预测,分别阐述了简化SCGM（1,1）模型、均值SCGMMV（1,1）模型、拓广SCGMa0（1,1）模型的原理和特点,并应用于实例。同时将精度较高的后两种模型和GM（1,1）模型进行固定权值和变权组合,进一步提高预测结果的精确度和可靠性。计算结果证明该方法预测准确,可靠性高,抗干扰性强,是中长期负荷预测新工具之一。     

基于SHGM估计方法的电力系统状态估计

电力系统状态估计作为一种应用软件，在整个能量[管理系统（EMS）中起着非常重要的作用，但是经典的估计方法在处理不良的异点时显得无能为力，为了解决这一问题，文中在简要分析映射统计概念及原理的基础上，用先辨识杠杆点的方法来进行估计，从而避免受到在这些点的不良数据的影响，仿真试验结果表明，该方法较一般的WLS状态估计方法在处理不良数据以及收敛性等方面具有更好的效果，并且它的算法是对较简单的基于WLS算法的修改，所以很容易实现。     

Computationally efficient algorithms for state estimation with ellipsoidal approximations

Computationally efficient algorithms for computing minimal‐volume ellipsoidal bounds on the state of a linear, discrete‐time dynamical system are presented. Ellipsoidally bounded noise is considered, rather than the scalar‐noise case usually treated; the latter is covered as a degenerate special case of the former. The algorithms have differing accuracy and computational requirements, resulting from differing specifications of the bounds. The paper compares these alternative approaches to computing ellipsoidal bounds on state, giving simulation results to illustrate the performance of the algorithms. Copyright © 2002 John Wiley & Sons, Ltd.     

基于支路带节点功率估计的配网状态估计实用算法

本文充分利用配电网的辐射性特点，采用基于单支路WLS估计的前推／回代估计方法对配电网进行状态估计，该方法在前推的逐支路估计过程中，对有后续支路的支路估计前有条件地加入末端节点功率估计，并通过分析提出一定原则对权值进行合理选取。对算例和实际辐射网的计算结果表明该方法收敛快速、估计精度高且编程简单，算法可靠、有效、实用。     

变电站-调度中心两级分布式状态估计

提出一种变电站—调度中心两级分布式状态估计方法,在变电站级实施基于基尔霍夫电流定律的零阻抗三相状态估计,在调度中心级对变电站上传的熟数据进行处理,实现全网单相状态估计。详细讨论了变电站—调度中心两级分布式状态估计的整体架构、算法、熟数据传输、对时及周期选择、传输协议、过渡期方案等,以说明该方法的可实施性。算例结果表明,该方法有利于在变电站内同时辨识三相坏数据和拓扑错误,减轻通信负担,并提高调度中心全网状态估计的可靠性。     

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。     

GRNN算法在电力系统状态估计中的应用

阐述了应用递推更新的样本数据训练广义回归神经网络进行状态估计的方法,分析了广义回归神经网络(GRNN)的结构与基本算法,提出平滑参数的确定方法,并给出确定输入神经元数目的方法.仿真结果表明,该网络训练方便快捷,估计精度高,能够满足在线估计的实时性要求.     

Fast moving horizon state estimation for discrete-time systems with linear constraints

Fast moving horizon state estimation for nonlinear discrete-time systems affected by disturbances is addressed by means of imperfect optimization at each time instant based on few iterations of the gradient, conjugate gradient, and Newton algorithms. Linear constraints on the state vector are taken into account through a projection on the subspace associated with such constraints. The stability of the estimation error for the resulting scheme is proved under suitable conditions. The effectiveness of the proposed approach is showcased via simulation results in comparison with moving horizon estimation based on complete optimization and extended Kalman filtering.     

Robust State Estimation for an Electricity-GasHeat Integrated Energy System Considering Dynamic Characteristics

The data for an energy management system (EMS) in an integrated energy system (IES) is obtained through state estimation. This is then the basis for optimal scheduling, protection and control. At present, the dynamic models of gas and heat networks are rarely considered in such state estimation, and the method lacks robustness. This paper develops dynamic state estimation models for gas and heat networks, and proposes a unified method for the electricity-gas-heat network, one which takes into account robustness while ensuring accuracy. First, the state transition equations in matrix form are formulated according to finite difference models considering the dynamic characteristics of the gas and heat networks. Then, combined with a quasi-steady state model of the electric power system, a unified state estimation method and multi-time-scale measurement strategy in the Kalman filter framework are proposed. In addition, the prediction accuracies of the electric power and gas systems are improved through adaptive adjustment. The kernel density estimation method is used to adjust the measurement weights and filter out bad data to ensure robust state estimation. Finally, simulation results show that the proposed method not only can improve the estimation accu-racy by improving prediction accuracy, but also is robust to various types of bad data.     

A distributed state estimation method for power systems incorporating linear and nonlinear models

High-voltage transmission networks are commonly equipped with phasor measurement units (PMU), and some of them are PMU observable. However, PMUs are seldom installed in distribution networks due to budget limitations. The state estimation equations of PMU observable areas are linear, while those of other areas remain nonlinear. This paper proposes a new distributed state estimation method for solving multi-area state estimation problems, in which linear models are used for high-voltage transmission network, while nonlinear models are adopted for other areas. In PMU observable area, we select coordinating variables as generalized cost functions which accurately represent the sensitivity between the linear SE objective function and boundary states. Consequently, SE results identical to a centralized estimator can be obtained without iterations at the coordination level. This paper presents the problem model and theoretical analysis of the proposed method, and shows its effectiveness by numerical tests.     

保留非线性的电力系统两级状态估计表

提出了一种新的状态估计方法，该法保留了测量方程的非线性，同时又利用了大系统分割方法，提高状态估计的计算速度。在１８节点系统上进行为数字仿真试验，得到了良好结果。