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.     

An adaptive freeway traffic state estimator

Real-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic nonlinear macroscopic traffic flow modeling and extended Kalman filtering. One major innovative aspect of the estimator is the real-time joint estimation of traffic flow variables (flows, mean speeds, and densities) and some important model parameters (free speed, critical density, and capacity), which leads to four significant features of the traffic state estimator: (i) avoidance of prior model calibration; (ii) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (iii) enabling of incident alarms; (iv) enabling of detector fault alarms. The purpose of the reported real-data testing is, first, to demonstrate feature (i) by investigating some basic properties of the estimator and, second, to explore some adaptive capabilities of the estimator that enable features (ii)-(iv). The achieved testing results are quite satisfactory and promising for further work and field applications.     

Moving horizon state estimation with global convergence using interval techniques: application to biotechnological processes

This work proposes an original method to estimate states in non-linear discrete-time systems with global convergence properties. The approach is based on the minimisation of a criterion (non-linear function, differentiable or not) that is the Euclidean norm of the difference between the estimated output and the measured output of the system over a considered time horizon. This method is based on an interval moving horizon state estimation method, called IMHSE, which is coupled to a technique of global optimisation of non-linear functions that uses interval arithmetic. The system states are described using a representation by interval numbers. The proposed technique is applied to biotechnological complex process models (solid substrate fermentation), and the results obtained through experimental and computer simulation demonstrate that this kind of estimator offers advantages over other observers and filters and can be easily implemented in an industrial context.     

网络攻击下的信息物理系统安全状态估计研究综述

近年来,信息物理系统在工业界的广泛应用引起了人们对系统安全问题的极大关注.信息物理系统对通信网络的深度依赖,使得网络攻击成为其中最为严峻的威胁之一,特别是那些能够干扰系统状态认知的攻击,因此,安全状态估计(即在遭受攻击时正确估计系统状态)已成为各界广泛关注的安全问题之一.此文旨在总结网络攻击下信息物理系统安全状态估计研究的进展.首先,介绍典型的网络攻击,并详细阐述在稀疏攻击下的安全状态估计问题.其次,探讨集中式安全状态估计和分布式安全状态估计的研究现状.在考虑稀疏攻击下安全状态估计问题的难点时,关键在于如何快速找到受到攻击的信道集合(这可能涉及到高计算复杂度).因此,将安全状态估计方法分为遍历搜索和非遍历搜索两大类,并对现有方法的优缺点进行归纳总结和详细阐述.然后,介绍稀疏攻击下信息物理系统安全状态能观性分析的研究现状.现有的研究结果表明:增加检测机制或先验知识可以缓解在稀疏攻击下安全状态估计所需的基础冗余度要求;同时,通过区分攻击和故障,也能有效降低传感器冗余度要求.最后,对信息物理系统安全状态估计仍然存在的问题进行展望,并提出一些可能的解决方向.     

Adaptive divided difference filtering for simultaneous state and parameter estimation

A novel adaptive version of the divided difference filter (DDF) applicable to non-linear systems with a linear output equation is presented in this work. In order to make the filter robust to modeling errors, upper bounds on the state covariance matrix are derived. The parameters of this upper bound are then estimated using a combination of offline tuning and online optimization with a linear matrix inequality (LMI) constraint, which ensures that the predicted output error covariance is larger than the observed output error covariance. The resulting sub-optimal, high-gain filter is applied to the problem of joint state and parameter estimation. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard DDF.     

Adaptive Control of A Servo System Based on Multiple Models

This paper presents an alternative technology for adaptive control of a DC motor servo system based on multiple models. A dynamic mechanical model of the controlled plant is built, where the unmeasurable variables can be estimated by a filter observer. According to the mechanical model, an adaptive controller is designed. Specific attention is given to the jumping parameters in the control process, which motivate the proposition of multiple models, including fixed models, identified model, and adaptive model, to approximate the global dynamic characteristics of the plant model. A model switching rule is proposed to select the optimal model matching the plant, and the identified and adaptive models are reset when switching occurs, minimizing the effect caused by jumping parameters. Simulation results demonstrate that the introduced scheme is superior to the conventional adaptive control in that it yields a significant improvement of transient stability and response speed as well as steady accuracy, guaranteeing better low‐speed performance.     

针对信息物理系统的自生成epsilon-隐性最优欺骗攻击策略设计

近年来,信息物理系统网络安全问题成为一大研究热点.以攻击者角度研究攻击设计问题可有效评估系统对网络攻击的脆弱性并为设计网络保护措施提供理论依据.鉴于此,在$epsilon$-隐性下研究针对信息物理系统远程状态估计的最优欺骗攻击设计问题.首先,与需要额外滤波器和历史数据在线计算真实新息的相关结果不同,提出一种利用离线生成的攻击信号篡改传感器测量值以降低系统性能的自生成攻击模型,使攻击更易实现.随后,推导得出该攻击下远程估计误差以量化攻击效果,并将攻击设计问题转化为多变量受限二次优化问题.不同于相关结果的恒定均值,模型采用更具一般性的时变均值,使优化问题包含更多决策变量且相关结果中的攻击优化方法无法直接求解.因此,利用K-L(Kullback-Leibler)散度和互信息的相关统计学性质将问题等价转化.再结合拉格朗日乘数法和所提出的参数特征关联覆盖法得到最优攻击策略,使其在$epsilon$-隐性下最大化远程估计误差.最后,通过仿真实例验证结果的有效性.     

A dynamic state estimator for power system dynamic security assessment

A global modularized dynamic state estimator is formulated to provide the data which will be required for future dynamic security assessment and dynamic security enhancement applications. The dynamic state estimator is global because it is capable of estimating small and large dynamic fluctuations in voltage angle and frequency for an entire area. The dynamic state estimator is composed of the sum of the static state estimate, obtained by using present hardware and algorithms and a modularized dynamic state estimate based on a linearized classical transient stability model with a stochastic load model. This dynamic state estimate component is modularized to (1) eliminate the need to measure or model external system generation and (2) to permit a reduction in computation requirements for (a) updating the linearized power system dynamic model and (b) for computing the state estimate. The modularization, which is accomplished by decoupling the linearized dynamic model for each subregion by measuring the power injections on lines connecting the subregion to the rest of the power system, causes the dynamic state estimate to be locally referenced. A global referencing procedure is proposed and discussed. A linearized stochastic model for the Michigan Electric Coordinated System is developed to illustrate the procedures proposed for developing the stochastic load model and determining the constant gain approximation for the governor turbine energy system dynamics. A summary of results on the performance of the Kalman state estimator is presented.     

一类非线性系统的自适应观测器

This paper investigates the means to design the ob- server for a class of nonlinear systems with Lipschitz conditions and unknown parameters.A new design approach of full-order state adaptive observer is proposed.The constructed observer could guarantee the error of state and the error of parameter estimation to asymptotically converge to zero.Furthermore,a numerical example is provided to verify the effectiveness of the observer.     

Robust state estimation for a class of uncertain time-delay systems

This paper considers a robust state estimation problem for a class of uncertain time-delay systems. In this problem, the noise and uncertainty are modelled deterministically via an integral quadratic constraint. The robust state estimation problem involves constructing the set of all possible states at the current time consistent with given output measurements and the integral quadratic constraint. This set is found to be an ellipsoid which is constructed via a linear state estimator.     

FDI攻击下异构互联信息物理系统分布式攻击估计

针对含有虚假数据注入(false data injection, FDI)攻击和传感器故障的异构互联信息物理系统,研究分布式攻击估计器设计问题.首先,将系统状态和传感器故障增广成等价的广义状态空间模型,利用该模型和子系统间的关联信息设计分布式攻击估计观测器.在分布式观测器的设计中,引入中间变量和输出估计误差反馈项,使观测器具有灵活的参数矩阵结构,适用于不同状态维度子系统组成的异构系统,实现对攻击信号和传感器故障的同时估计.其次,对动态误差系统进行稳定性分析,利用H∞性能来抑制攻击信号和外部干扰对估计效果的影响,同时以线性矩阵不等式的形式给出观测器增益矩阵的求解方法.最后,通过数值仿真和对比仿真验证所提攻击估计方法的可行性.     

Observer design for generalized state space systems with unknown inputs

The following four major aspects for the observer design problem for generalized state space systems with unknown inputs are resolved: Necessary and sufficient conditions for the problem to have a solution, the order of the minimal observer, the properties of the closed-loop system (separation principle) and general analytical expressions of the minimal order observer matrices. All above results are first in the field.     

Discrete state estimators for systems on a lattice

We address the problem of estimating discrete variables in a class of deterministic transition systems in which the continuous variables are available for measurement. We propose a novel approach to the estimation of discrete variables using lattice theory that overcomes some of the severe complexity issues encountered in previous work. The methodology proposed for the estimation of discrete variables is general as it is applicable to any observable system. Extensions generalize the approach to nondeterministic transition systems. The proposed estimator is finally constructed for a multi-robot system involving two teams competing against each other.     

基于大数据的智能电网状态远程监测方法

智能电网作为一种智能化电力传输载体,在电力供应环节发挥了重要作用,因此保证其正常运行具有重要的现实意义。当前智能电网状态远程监测多与智能算法相结合,通过智能算法完成状态评估,常见的智能算法有神经网络、决策树以及支持向量机等,但这三种算法应用下,空间复杂度与时间复杂度较大。针对上述问题,提出一种基于大数据的自适应免疫粒子群算法智能电网状态远程监测方法。方法首先利用量测工具对智能电网状态信息量进行采集,然后对采集到的信息量进行处理,包括数据清洗、数据去噪、数据消减、数据标准化,最后利用自适应免疫粒子群算法实现智能电网健康状况评估。结果表明:与神经网络、决策树以及支持向量机三种算法相比,自适应免疫粒子群算法运行下,产生的空间复杂度与时间复杂度最小,分别为247.7 byte和154 s。