Asynchronous distributed state estimation based on a continuous‐time stochastic model

In this paper, the problem of state estimation in an asynchronous distributed multi‐sensor estimation (ADE) system is considered. In such an ADE system, the state of a plant of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs fusion of data from its local sensor and other (remote) processors to compute possibly best state estimates. In performing data fusion, however, two important issues need to be addressed, namely, the problem of asynchronism of local processors and the one of unknown correlation between asynchronous data in local processors. Consequently, there are two main contributions proposed in this paper. The first is a method to deal with asynchronous discrete‐time data based on a continuous‐time stochastic plant model. The second contribution is an asynchronous distributed data‐fusion algorithm. Simulated experiments illustrate the effectiveness of the proposed ADE approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Decentralized stochastic adaptive control of interconnected systems

This paper considers the stochastic adaptive control problem for a class of large-scale systems formed by arbitrary interconnection of subsystems with unknown parameters and non-linearities. For the estimation of the unknown parameters of the local controllers, stochastic approximation algorithms are used. Conditions sufficient for global stability of the overall system are established. It is shown that the overall tracking error is bounded by a quantity depending on the size of interconnections.     

Neuro-adaptive output-feedback optimized stochastic control for the active suspension systems with state constraints

This article investigates an adaptive neural network (NN) output-feedback optimal control design problem for active suspension systems (ASSs) with stochastic disturbance. The ASSs under consideration contain the characteristics of spring nonlinear dynamics, unmeasured states, and state constraints. The NNs are developed to approximate the unknown nonlinear functions. Meanwhile, observer-based output feedback control design method is proposed based on the adaptive backstepping technique. Furthermore, the stability of the closed-loop system is demonstrated by constructing the barrier Lyapunov function, thus ensuring that the full-state constraints are not exceeded. In particular, the simulation validations are given for the cases of bump, C-class, and D-class road displacements inputs. Finally, the simulation results verify the effectiveness of the studied control strategy.     

Formulation, computation and improvement of steady state security margins in power systems. Part II: Results

A steady state security margin for a particular operating point can be defined as the distance from this initial point to the secure operating limits of the system. Four of the most used steady state security margins are the power flow feasibility margin, the contingency feasibility margin, the load margin to voltage collapse, and the total transfer capability between system areas. This is the second part of a two part paper. Part I has proposed a novel framework of a general model able to formulate, compute and improve any steady state security margin. In Part II the performance of the general model is validated by solving a variety of practical situations in modern real power systems. Actual examples of the Spanish power system will be used for this purpose. The same computation and improvement algorithms outlined in Part I have been applied for the four security margins considered in the study, outlining the convenience of defining a general framework valid for the four of them. The general model is used here in Part II to compute and improve: (a) the power flow feasibility margin (assessing the influence of the reactive power generation limits in the Spanish power system), (b) the contingency feasibility margin (assessing the influence of transmission and generation capacity in maintaining a correct voltage profile), (c) the load margin to voltage collapse (assessing the location and quantity of loads that must be shed in order to be far away from voltage collapse) and (d) the total transfer capability (assessing the export import pattern of electric power between different areas of the Spanish system).     

Formulation, computation and improvement of steady state security margins in power systems. Part I: Theoretical framework

A steady state security margin for a particular operational point can be defined as the distance from this initial point to the secure operational limits of the system. Four of the most used steady state security margins are the power flow feasibility margin, the contingency feasibility margin, the load margin to voltage collapse, and the total transfer capability between system areas. A comprehensive literature survey has shown that these security margins have been studied separately. This fact has suggested to the authors the possibility of researching a common analysis framework valid for all of them. This is the first part of a two-part paper. In part I, a novel mathematical formulation valid to address the study of any steady state security margin is proposed. The developed general approach is presented in three steps: (a) formulation, (b) computation, and (c) improvement of security margins. In part II, the performance of the proposed approach when used to compute and improve the aforementioned steady security margins is illustrated through its application to the Spanish power system. Results denote that this approach can be a useful tool to solve a variety of practical situations in modern real power systems.     

Stability of the equilibria of adaptive systems with leakage estimator

We study the stability of the equilibria of the differential equations that describe an adaptive controller in closed loop with a linear time-invariant (LTI) undermodelled plant when the parameter update law is a leaky gradient, i.e. a s?-modified estimator. Hsu and Costa studied the full-order case and showed that under certain limiting conditions the resulting dynamic system has three, possibly unstable, equilibrium points. Here we provide a modest extension to that work by further characterizing the class of undermodelled LTI plants for which the equilibria exist and are (un)stable Interestingly enough, it is shown that the equilibria are stable iff a given compensator stabilizes the plant. This compensator is, up to the plant ‘steady state gain’, known to the designer.     

Fuzzy approximation-based adaptive control of nonstrict feedback stochastic nonlinear systems with time-varying state constraints

This article develops an approximation-based fuzzy control scheme for nonstrict feedback stochastic nonlinear systems (NFSNS) with time-varying state constraints. The difficulty in constructing controller is how to conquer the algebraic loop problem caused by nonstrict feedback structure, as well as prevent the state constraints from violating. To dispose the time-varying state constraints, time-varying barrier Lyapunov function is incorporated into the backstepping design framework. The lumped uncertainties of NFSNS are approximated by the fuzzy logic systems. By virtue of fuzzy basis function, the algebraic loop problem is effectively handled. Theoretical analysis shows that the predefined state constraints are not violated and all signals of the closed-loop systems are bounded. Finally, simulation results substantiate the validity of the devised method.     

Adaptive state feedback stabilization of more general stochastic high‐order nonholonomic systems

This paper investigates adaptive state feedback stabilization for a class of more general stochastic high‐order nonholonomic systems. By constructing the appropriate Lyapunov function, skillfully combining parameter separation, sign function, and backstepping design methods, an adaptive state feedback controller is designed to eliminate the phenomenon of uncontrollability and guarantee global asymptotic stability in probability of the closed‐loop system. Two simulation examples are used to demonstrate the effectiveness of this method.     

Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems

With the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation.     

Adaptive dynamic surface control of stochastic nonstrict-feedback constrained nonlinear systems with input and state unmodeled dynamics

In this paper, the issue of adaptive neural control is discussed for a class of stochastic nonstrict-feedback constrained nonlinear systems with input and state unmodeled dynamics. A dynamic signal produced by the first-order auxiliary system is employed to deal with the dynamical uncertain terms. Radial basis function neural networks are used to reconstruct unknown nonlinear continuous functions. With the help of the mean value theorem and Young's inequality, only one learning parameter is adjusted online at recursive each step. Using the hyperbolic tangent function as nonlinear mapping, the output constrained stochastic nonstrict-feedback system in the presence of unmodeled dynamics is transformed into a novel unconstrained stochastic nonstrict-feedback system. Based on dynamic surface control technology and the property of Gaussian function, adaptive neural control is developed for the transformed stochastic nonstrict-feedback system. The output abides by stochastic constraints in probability. By the Lyapunov method, all signals of the closed-loop control system are proved to be semi-global uniform ultimate bounded (SGUUB) in probability. The obtained theoretical findings are verified by two numerical examples.     

基于一致性理论的多区域电力系统分布式状态估计

随着电网规模不断扩大,传统集中式状态估计方法的数据通信与存储任务重、计算量大,难以满足现代电力系统状态估计需求。在计及系统状态估计非线性的基础上,将电力系统划分为若干个不重叠的子区域,并利用拉格朗日乘子法对状态估计方程进行解耦,建立电力系统多区域非线性状态估计模型。基于一致性理论建立全分布式状态估计方法对模型进行求解,该方法无需状态估计控制中心,只需各子区域交换一致性变量和边界节点的状态变量信息,各子区域便可平行独立地计算本地状态变量估计值,较集中式状态估计均衡了通信及计算负担。IEEE 14节点系统仿真结果验证了所提分布式状态估计方法的有效性。     

交直流系统暂态仿真中换流器的改进准稳态模型

当交流系统发生三相不对称故障时,忽略直流系统负序分量的准稳态模型将给交直流混合系统的仿真带来较大的误差。文中考虑了整流器、逆变器的具体差异,给出了换相电压不对称情况下的直流电压计算方法,编制了换流器的仿真程序,并应用到考虑直流系统负序分量的换流器改进准稳态模型的混合系统暂态计算中,取得了令人满意的结果。     

发电机组状态检修的研究

传统发电机组检修有许多不足的地方,提出了基于马尔可夫链状态估计模型的状态检修策略。机组状态估计模型用一步转移概率描述相邻状态概率向量之间的关系,并用步伐因子考虑机组所带负荷对转移概率的影响。在预估机组临界故障状态的基础上,以检修费用和电量收益损失总和最小为目标函数确定最佳检修时间。     

Robust fault tolerant control for a class of networked control systems with state delay and stochastic actuator failures

This paper is concerned with the robust fault tolerant controller design of networked control systems (NCSs) with state delay and stochastic actuator failures. By utilizing the input delay approach, an equivalent continuous‐time generalized time delay system in both state and input is obtained. By applying a delay decomposition approach, the information of the delayed plant states can be taken into full consideration, and new delay‐dependent sufficient conditions that ensure the asymptotic mean‐square stability of NCSs with stochastic actuator failures are derived in terms of linear matrix inequalities (LMIs). It is realized by employing a new Lyapunov–Krasovskii function in the decomposed integral intervals and directly handle the inversely weighted convex combination of quadratic terms of integral quantities with reciprocally convex combination technique. Moreover, the proposed approach involves neither slack variable nor any model transformation. A numerical example is provided to demonstrate the effectiveness and less conservatism of the proposed method.Copyright © 2012 John Wiley & Sons, Ltd.     

复杂配电系统电压跌落状态估计可观性分析

电压跌落状态估计算法(SSE算法)是一种基于单电源辐射状网络结构的二阶曲线拟合算法。但是目前众多分布式发电接入及小电厂并网发电使得配电网络结构呈现复杂化、多电源化,传统电压跌落状态估计算法无法直接使用。针对该问题,提出了电压跌落状态估计可观性的概念,并且从故障电流叠加的角度出发,构造了电压跌落状态估计可观性的数学模型,合理地解决了部分故障路段不可观的问题,使得SSE算法可以应用到多电源复杂配电系统中。     

标准遗传算法的收敛性分析及其趋近稳态的速度估计

应用齐次Ｍｒｋｏｖ链仔细分析了标准遗传算法（ＳＧＡ）趋近于稳态的过程,给出其稳态分配的具体表示形式;同时得到了更广泛和严格意义上的与ＳＧＡ控制参数相联系的到达稳态的速度估计,其结果对于其他全局收敛ＧＡ的收敛性和收敛速度研究都有借鉴意义。     

Modeling and design of networked control systems using a stochastic switching systems approach

A networked control system (NCS) is a control system in which plants, sensors, controllers, and actuators are connected through communication networks. In this paper, we consider NCSs modeled by stochastic switching systems, and propose a new method for modeling and optimal control. First, a recursive representation of the expected value of the state is proposed. Next, after an over‐approximation of this recursive representation is derived, the optimal control problem is reduced to a linear programming problem. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method provides us an easy‐to‐use control method for NCSs. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

On state equations of two‐level quantum systems in a thermal environment

The state equations of open two‐level quantum systems, which form the building blocks of quantum cellular neural networks, are studied in arbitrary representations. It is shown that the dissipation matrix, that under the usual assumptions is diagonal in the energy representation, such remains if and only if the coupling between the states induced by an external field is real and infinitesimal. Copyright © 2004 John Wiley & Sons, Ltd.