基于多源数据融合的配网状态估计

配网中多种量测设备所采集的异构异源数据共同构成了用于状态估计的数据源,对量测数据的最大化利用以及混合量测的充分融合是提升状态估计精度的首要任务。根据配网中数据量测装置的装设情况和量测数据的特点,提出于一种基于改进LSTM算法的SCADA缺失值填充算法,并考虑混合量测融合过程中的时标统一、权值分配问题,实现了SCADA数据的充分利用以及混合量测的精度提升。对提升配电网状态估计精度有一定的积极意义,搭建IEEE-33节点系统,验证了所提方法的可行性。     

基于多传感器信息融合在状态估计上的应用

利用测量系统采用单一传感器对测量系统获取观测数据,通过经典卡尔曼滤波算法对观测数据进行估计.在对复杂多变的环境中利用多种传感器所测量的数据进行融合分析,针对不同结果的观测数据,建立数学模型,用方差大小来衡量每个传感器的精度性,对获取到不同的观测数据进行观测融合,采用集中式融合和分布式融合方法,分布式融合运用矩阵分析把高...     

应用于状态监测的多传感器融合估计

在状态监测的工程实际中, 使用多个同类传感器进行在线测量可以得到更为准确的状态估计．但各传感器测量噪声会出现相关的情况, 而且很难得到相关测量噪声的方差矩阵的精确值, 测量系统往往是不确定的．本文根据系统测量将系统分解为确定和不确定扰动两部分, 分别进行估计, 然后将两者的融合估计结果相加得到了最优鲁棒的融合估计．针对确定部分, 利用同类传感器的测量方差为Pei-Radman矩阵的特性, 通过求解测量噪声方差矩阵的最大特征值得到了一种简便的最优融合估计算法, 该算法避免了求解方差矩阵的逆的过程．针对不确定     

面向城市道路交通状态估计的数据融合研究

实时道路交通状态估计是ATMS和ATIS的重要内容。布设于城市道路网络中的各类检测器提供了丰富实时的动态信息。针对目前我国各检测器间相互独立形成信息孤岛、数据参数多样、结构迥异、采样周期和精度不一等现状,采用贝叶斯估计、模糊逻辑等数据融合方法建立多源异构交通信息三层次融合体系,得到精度更高、可靠性更强的交通信息。实例证明,数据融合适用于城市道路交通状态估计。     

多传感器状态融合估计在雷达跟踪中的应用

采用Carlson最优数据融合准则,将基于Kalman滤波的多传感器状态融合佑计方法应用到雷达跟踪系统.仿真实验表明,多传感器Kalman滤波状态融合佑计误差小于单传感器Kalman滤波得出的状态佑计误差,验证了方法对雷达跟踪的有效性.     

多传感器数据融合下的无人机高度扩张状态观测器设计

无人机在飞行作业时需要精确稳定的高度估计, 针对传感器噪声以及气流和温度变化对无人机高度估计影响的问题, 本文提出一种基于多传感器数据融合的扩张状态观测器设计方法. 首先, 建立无人机高度和传感器噪声模型并对其进行简化处理; 其次, 针对位置传感器、气压计和惯性测量单元的传感器特性, 结合多传感器数据融合方法设计扩张状态观测器估计出无人机高度、z轴速度和总扰动, 并将总扰动估计值反馈给控制系统; 然后, 根据四旋翼无人机非线性数学模型进行数值仿真, 仿真表明本文设计方法的合理性和有效性; 最后, 通过实物测试验证了所设计的扩张状态观测器能有效估计和补偿扰动并对传感器噪声有良好的抑制能力.     

基于多传感器的信息数据融合技术在电力系统中的应用

论述了多传感器信息数据融合技术的概念、原理、方法及当前数据融合研究中存在的主要问题;以信息融合技术为研究对象,结合电力系统的实际特点,提出多传感器信息数据融合技术在电力系统各领域应用中亟待解决的问题、解决方案及发展方向。     

基于OBE算法的自适应集员状态估计

研究了具有椭球集合描述的离散时间动态线性系统的状态估计问题.从提高计算的有效性和可实现性的角度出发,通过在不同的更新阶段采用优化定界椭球(OBE)算法,提出了一种新颖的解决状态估计的方法.通过与ROBP(recursive state bounding by parallelotopes)算法和Kalman滤波的仿真比较,验证了本方法的性能.     

估计融合算法的一类敏感指标研究

为了改善在复杂多变的环境下多源信息融合的准确性和鲁棒性,可在融合过程中进行算法管理,自适应配置算法的方法实现。在算法配置中反馈所需的条件采用算法敏感指标进行评价。在状态估计敏感指标OSPA距离测度基础上,提出估计融合中的敏感指标GOSPA距离测度,GOSPA距离将真实航迹和全局航迹之间的误差分离成航迹距离误差和航迹关联误差。通过对比实验表明,估计融合中的全局OSPA测度指标对航迹融合算法的性能评估是敏感的。     

A separated bias identification and state estimation algorithm for nonlinear systems

A computational algorithm for the identification of biases in discrete-time, nonlinear, stochastic systems is derived by extending the separate bias estimation results for linear systems to the extended Kalman filter formulation. The merits of the approach are illustrated by identifying instrument biases using a terminal configured vehicle simulation.     

Unbiased minimum-variance input and state estimation for linear discrete-time systems

This paper addresses the problem of simultaneously estimating the state and the input of a linear discrete-time system. A recursive filter, optimal in the minimum-variance unbiased sense, is developed where the estimation of the state and the input are interconnected. The input estimate is obtained from the innovation by least-squares estimation and the state estimation problem is transformed into a standard Kalman filtering problem. Necessary and sufficient conditions for the existence of the filter are given and relations to earlier results are discussed.     

Structural parameter estimation in power systems

A new class of algorithms for the estimation of structural parameters of a continuous-time linear system excited by random natural disturbances is presented in the paper. All these algorithms are based on fitting the autocorrelation function of the system output; differences among them arise from the various possible formulations of the fit-criterion. Thus, the asymptotic statistical properties of the estimate are analyzed in order to have a choice tool among the class of algorithms and to compare them with other existing estimation methods. A further relevant subject is the statement of a robust test to verify the correctness of the tentative model assumed for the sake of the estimation procedure. Then the above algorithm is applied to the problem of estimating structural parameters (i.e. natural frequencies and damping factors) of the Italian and Yugoslavian power systems by recording some main electrical quantities during the normal operation of the system.Capability of dealing with structural systems affected by an unknown number of oscillatory modes and simplicity of use by non-statistical people are interesting features of the present approach, emphasized by the application.     

目标跟踪状态估计排序粒子群算法研究

基于视觉的无人机地面目标跟踪状态估计为非线性滤波问题,针对使用一般粒子滤波算法存在粒子退化和计算量大的缺陷问题,提出了一种基于排序的粒子滤波算法,对粒子依误差大小进行排序并计算粒子权重。仿真试验表明,该方法减轻了粒子贫化的影响,提高了状态估计精度。     

A survey on cyber attacks against nonlinear state estimation in power systems of ubiquitous cities

It is well-known that critical infrastructures would be targets for cyber attacks. In this paper, we focus on the power systems (i.e. smart grids) in ubiquitous cities, where every meter is linked to an information network through wireless networking. In a smart grid system, information from smart meters would be used to perform a state estimation in real time to maintain the stability of the system. A wrong estimation may lead to disastrous consequences (e.g. suspension of electricity supply or a big financial loss). Unfortunately, quite a number of recent results showed that attacks on this estimation process are feasible by manipulating readings of only a few meters. In this paper, we focus on nonlinear state estimation which is a more realistic model and widely employed in a real power grid environment. We category cyber attacks against nonlinear state estimation, and review the mechanisms behind. State-of-the-art security measures to detect these attacks are discussed via sensor protection. Hope that the community would be able to come up with a secure system architecture for ubiquitous cities.     

不同特征数字信号的谱估计算法性能分析

通过实验八种经典和现代的数字信号谱估计算法,并分析了这几种谱估计算法对不同特性信号主要包括周期信号、声音信号和随机信号的影响和差异,从这些算法所表现出的特征和差异中得出针对不同特征信号的谱估计算法性能的优劣性.     

Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties

In this work, we propose a distributed moving horizon state estimation (DMHE) design for a class of nonlinear systems with bounded output measurement noise and process disturbances. Specifically, we consider a class of nonlinear systems that are composed of several subsystems and the subsystems interact with each other via their subsystem states. First, a distributed estimation algorithm is designed which specifies the information exchange protocol between the subsystems and the implementation strategy of the DMHE. Subsequently, a local moving horizon estimation (MHE) scheme is designed for each subsystem. In the design of each subsystem MHE, an auxiliary nonlinear deterministic observer that can asymptotically track the corresponding nominal subsystem state when the subsystem interactions are absent is taken advantage of. For each subsystem, the nonlinear deterministic observer together with an error correction term is used to calculate a confidence region for the subsystem state every sampling time. Within the confidence region, the subsystem MHE is allowed to optimize its estimate. The proposed DMHE scheme is proved to give bounded estimation errors. It is also possible to tune the convergence rate of the state estimate given by the DMHE to the actual system state. The performance of the proposed DMHE is illustrated via the application to a reactor-separator process example.     

Nonlinear bounded-error state estimation of continuous-time systems

This paper presents a first study on the application of interval analysis and consistency techniques to state estimation of continuous-time systems described by nonlinear ordinary differential equations. The approach is presented in a bounded-error context and the resulting methodology is illustrated by an example.     

Extension of unbiased minimum-variance input and state estimation for systems with unknown inputs

This paper extends the existing results on joint input and state estimation to systems with arbitrary unknown inputs. The objective is to derive an optimal filter in the general case where not only unknown inputs affect both the system state and the output, but also the direct feedthrough matrix has arbitrary rank. The paper extends both the results of Gillijns and De Moor [Gillijns, S., & De Moor, B. (2007b). Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 43, 934–937] and Darouach, Zasadzinski, and Boutayeb [Darouach, M., Zasadzinski, M., & Boutayeb, M. (2003). Extension of minimum variance estimation for systems with unknown inputs. Automatica, 39, 867–876]. The resulting filter is an extension of the recursive three-step filter (ERTSF) and serves as a unified solution to the addressed unknown input filtering problem. The relationship between the ERTSF and the existing literature results is also addressed.     

Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough

This paper extends previous work on joint input and state estimation to systems with direct feedthrough of the unknown input to the output. Using linear minimum-variance unbiased estimation, a recursive filter is derived where the estimation of the state and the input are interconnected. The derivation is based on the assumption that no prior knowledge about the dynamical evolution of the unknown input is available. The resulting filter has the structure of the Kalman filter, except that the true value of the input is replaced by an optimal estimate.