Complete stability of feedback CNNs with dynamic memristors and second‐order cells

The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary cells with an ideal capacitor and an ideal flux‐controlled memristor. It is supposed that during the analogue computation of the CNN the memristors behave as dynamic elements, so that each dynamic memristor (DM)‐CNN cell is described by a second‐order differential system in the state variables given by the capacitor voltage and the memristor flux. The proposed networks are called DM‐CNNs, that is CNNs using a dynamic (D) memristor (M). After giving a foundation to the DM‐CNN model, the paper establishes a fundamental result on complete stability, that is convergence of solutions toward equilibrium points, when the DM‐CNN has symmetric interconnections. Because of the presence of dynamic memristors, a DM‐CNN displays peculiar and basically different dynamic properties with respect to standard CNNs. First of all a DM‐CNN computes during the time evolution of the memristor fluxes, instead of the capacitor voltages as for a standard CNN. Furthermore, when a steady state is reached, the memristors keep in memory the result of the computation, that is the limiting values of the fluxes, while all memristor currents and voltages, as well as all currents, voltages, and power in the DM‐CNN vanish. Instead, for standard CNNs, currents, voltages, and power do not drop off when a steady state is reached. Copyright © 2016 John Wiley & Sons, Ltd.     

On complete stability of linear and quadratic programming neural networks

The paper addresses complete stability (CS) of the important class of neural networks to solve linear and quadratic programming problems introduced by Kennedy and Chua (IEEE Trans. Circuits Syst., 1988; 35 : 554). By CS it is meant that each trajectory converges to a stationary state, i.e. an equilibrium point of the neural network. It is shown that the neural networks in (IEEE Trans. Circuits Syst., 1988; 35 : 554) enjoy the property of CS even in the most general case where there are infinite non‐isolated equilibrium points. This result, which is proved by exploiting a new method to analyse CS (Int. J. Bifurcation Chaos 2001; 11 : 655), extends the stability analysis by Kennedy and Chua (IEEE Trans. Circuits Syst., 1988; 35 : 554) to situations of interest where the optimization problems have infinite solutions. Copyright © 2002 John Wiley & Sons, Ltd.     

Heteroassociative memories via cellular neural networks

In this paper a synthesis procedure for heteroassociative memories using Cellular Neural Networks (CNNs) is presented. The suggested method, by assuring the condition of symmetry of the interconnection matrix, guarantees the complete stability of the designed network, besides providing that all the stored patterns correspond to asymptotically stable equilibrium points. Numerical examples are carried out to show the behaviour of the designed memory with respect to input perturbations. Moreover, the storage capacity and the presence of spurious equilibria have been investigated. © 1998 John Wiley & Sons, Ltd.     

Convergence and stability analysis of a digital automatic gain control

This paper is dedicated to the theoretical analysis of a digital automatic gain control (AGC), which feedbacks according to the desired output direct current (DC). First, we mathematically model the AGC, with the input signal interfered by AWGN. As it turns out, the loop model is a first‐order varying‐coefficient stochastic difference equation, and its solution consists of two parts, that is, the transient state and the steady state, which respectively explain the convergence and stability of the AGC. As is shown in the paper, the properties of convergence and stability of AGC are mutually contradictory, and both are determined by a key parameter in the loop, and the parameter is defined as the feedback sensitivity of the loop. The results of the paper are validated by numerical simulation, and they are of theoretical guide for practical digital AGC implementation on Application Specific Integrated Chips (ASICs) or Field Programmable Gate Arrays (FPGAs). Copyright © 2013 John Wiley & Sons, Ltd.     

Asymptotic stability of a class of adaptive systems

In this short paper we give conditions for uniform asymptotic stability of the equilibrium of a class of non-linear non-homogeneous differential equations. This result is of interest in adaptive systems theory, where this strong stability allows us to establish some robustness properties.     

Comparison of convergence and stability properties for the state and output solutions of neural networks

A typical neuron cell is characterized by the state variable and the neuron output, which is obtained by passing the state through a nonlinear active device implementing the neuron activation. The paper introduces a wide class of neural networks for which the state solutions and the output solutions enjoy the same convergence and stability properties. The class, which includes as a special case the standard cellular neural networks, is characterized by piecewise‐linear Lipschitz continuous neuron activations, Lipschitz continuous (possibly) high‐order interconnections between neurons and asymptotically stable isolated neuron cells. The paper also shows that if we relax any of the assumptions on the smoothness of the neuron activations or interconnecting structure, or on the stability of the isolated neuron cells, then the equivalence between the convergence properties of the state solutions and the output solutions is in general no longer guaranteed. To this end, three relevant classes of neural networks in the literature are considered, where each class violates one of the assumptions made in the paper, and it is shown that the state solutions of the networks enjoy stronger convergence properties with respect to the output solutions or viceversa. Copyright © 2010 John Wiley & Sons, Ltd.     

Further results on stable weighted multiple model adaptive control: Discrete‐time stochastic plant

A new weighting algorithm is proposed to relax the convergence conditions and to improve the convergence rate for weighted multiple model adaptive control systems. The stability and convergence of the corresponding weighted multiple model adaptive control systems of two types of stochastic plants, one is linear time‐invariant system (LTI) with unknown parameters, the other is linear time‐varying system with jumping parameters, are proved. Finally, some simulation results are presented to verify the effectiveness of the proposed weighting algorithm and the performance of the closed‐loop control system. Copyright © 2015 John Wiley & Sons, Ltd.     

A general stability criterion for switched linear systems having stable and unstable subsystems

We report conditions on a switching signal that guarantee that solutions of a switched linear system converge asymptotically to zero. These conditions apply to continuous, discrete‐time and hybrid switched linear systems, those having both stable subsystems and mixtures of stable and unstable subsystems. Copyright © 2012 John Wiley & Sons, Ltd.     

Łojasiewicz inequality and exponential convergence of the full‐range model of CNNs

This paper considers the Full‐range (FR) model of Cellular Neural Networks (CNNs) in the case where the signal range is delimited by an ideal hard‐limiter nonlinearity with two vertical segments in the i?v characteristic. A ?ojasiewicz inequality around any equilibrium point, for a FRCNN with a symmetric interconnection matrix, is proved. It is also shown that the ?ojasiewicz exponent is equal to . The main consequence is that any forward solution of a symmetric FRCNN has finite length and is exponentially convergent toward an equilibrium point, even in degenerate situations where the FRCNN possesses non‐isolated equilibrium points. The obtained results are shown to improve the previous results in literature on convergence or almost convergence of symmetric FRCNNs. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust stochastic convergence and stability of neutral‐type neural networks with Markovian jump and mixed delays

The robust stochastic convergence and stability in mean square are investigated for a class of uncertain neutral‐type neural networks with both Markovian jump parameters and mixed delays. First, by employing the Lyapunov method and a generalized Halanay‐type inequality for stochastic differential equations, a delay‐dependent condition is derived to guarantee the state variables of the discussed neural networks to be globally uniformly exponentially stochastic convergent to a ball in the state space with a prespecified convergence rate. Next, by applying the Jensen integral inequality and a novel reciprocal convex lemma, a delay‐dependent criterion is developed to achieve the globally robust stochastic stability in mean square. With some parameters being fixed in advance, the proposed conditions are all expressed in terms of LMIs, which can be solved numerically by employing the standard MATLAB LMI toolbox package. Finally, two illustrated examples are given to show the effectiveness and less conservatism of the obtained results over some existing works. Copyright © 2014 John Wiley & Sons, Ltd.     

电力系统稳定及稳控措施系统配置分析

通过对湖南电网稳定计算分析和相应稳控措施的系统配置分析,从满足《电力系统安全稳定导则》的要求和提高电网输电能力方面,讨论了配置电力系统稳控措施的必要性,总结了配置稳控措施所需的稳定计算分析的要求和方法,归纳了稳控措施的系统配置原则和要点。     

Stability of adaptive delta modulators with forgetting factor and constant inputs

Motivated by applications to feedback control over communication networks where the actuation and feedback signals are transmitted over communication channels, we study the stability of Adaptive Delta Modulators (ADM) when the coded signal is a constant. The importance of such a setting arises because a common control task is to track a dc input. It is known that a standard accumulator‐based ADM has the following highly undesirable characteristic: virtually all combinations of the algorithm parameters result in 4‐cycles, and the avoidance of 4‐cycles requires a nongeneric initialization. Further, the steady state oscillations that generically arise in the course of these cycles can have amplitudes that can be arbitrarily close to the initial error. Consequently, we study the use of a forgetting factor in the ADM loop, and provide a detailed stability analysis and design guidelines. Intuitively, adding a forgetting factor to the classical ADM algorithm prevents 4‐periodic cycles from occurring by damping them. In particular, we show that for suitably chosen design parameters, the ADM with forgetting factor can track a constant signal arbitrarily closely under mild assumptions. We provide simulations to demonstrate how much better the modified algorithm performs relative to the original ADM algorithm in a remote control setting. Copyright © 2011 John Wiley & Sons, Ltd.     

Weak bus-constrained PMU placement for complete observability of a connected power network considering voltage stability indices

Phasor measurement units (PMUs) are preferred for installation at weak buses in a power network. Therefore, the weak buses need to be located and the strategic locations of PMUs identified to ensure network observability. Thus, the primary aim of this work is to identify the placements of the maximum number of PMUs installed at the weak buses in the electrical network. The voltage collapse proximity indicator, line stability index, fast voltage stability index, and a new voltage stability indicator utilizing load flow measurement are used to determine the weak buses. A novel deterministic methodology based on a binary-integer linear programming model is then proposed to determine the optimal locations of PMUs. The effect of a single PMU outage considering the weak buses is also demonstrated. The effectiveness of the developed approach is tested and validated on the standard IEEE 14-, 118-, 300-, and New England 39-bus systems. The obtained results are also compared to those using different weak bus methodologies.     

基于稳定裕度指标的暂态电压稳定分析

基于稳定域边界二次近似,提出了暂态电压稳定裕度指标,并分析了计及感应电动机机电暂态过程的单负荷无穷大系统的暂态电压稳定性.仿真表明:该指标具有准确度高及易于计算机实现等特点,具有较好的工程实用性,并可应用于判断系统暂态电压稳定裕度和估计暂态电压稳定的临界切除时间以及故障排序.     

全光网络的简介及展望

简要介绍全光网络的基本概念（全光交换、光交叉连接、全光中继、光复用／去复用）,全光网的网络结构与业务类型,以及全光网络的进展状况。     

全机械化展放导线施工工艺研究

电网建设的深入发展,对施工工艺的要求不断提高,完善的施工工艺可以保证电网建设的质量及安全。架线施工的工艺水平对施工质量、安全的影响很大,全机械化施工工艺对改善工程质量,降低安全风险,减轻工人劳动强度,提高施工效率具有积极的作用。     

同步电动机的动态和瞬态稳定极限

利用同步电动机的状态方程,研究了同步电动机在突加负载时的动态过程,经过多次迭代,分别得出了动态稳定极限和“首次摆动”瞬态稳定极限,并与由瞬态功角特性和等面积法则得到的瞬态稳定极限进行了比较,另外,还研究机组转动惯量对动态稳定极限和突加负载时动态过程的影响,最后得出了一些有用的结论。     

静态功角稳定和静态电压稳定判据比较分析

指出用传统的静态功角稳定的静态稳定储备系数评估系统静态功角稳定性的不足在于其过于乐观,并容易造成认识上的误区;静态功角稳定和电压稳定的极限均是求取系统潮流的极限值,但静态功角稳定的推导是建立在单机无穷大系统基础上,静态电压稳定的推导则是建立在电源电压恒定的基础上。从电能物理传输原理上对静态功角稳定判据和静态电压稳定判据进行了推导,从而纠正了由于2个不同假设条件推导出的静态电压稳定和静态功角稳定判据的不足,得出了统一的结论。指出通常当线路两端相位差在45°以内时失稳是静态电压稳定失稳,而超过45°是静态功角稳定失稳;传输感性无功功率影响系统的静态功角稳定,将造成系统静态功角稳定的下降和传输功率的下降,而传输容性无功功率正好相反。     

Relationships between voltage and angle stability of power systems

This paper discusses modelling and theoretical issues associated with voltage and angle stability of power systems. A time-scale decomposition is performed to illustrate how the critical modes can be identified with reduced-order models and the bifurcation phenomena can be explained with these low order models. Examples are given for single and multi-machine systems.     

黑龙江500 kV电网安全稳定控制系统的实现

为解决500kV电网存在的稳定问题,保障系统可靠运行,在黑龙江电网西部、中部和东部按照统一设计,都装设了CSS-100BE区域型安全稳定控制系统。详细介绍了该系统的总体配置、硬件结构、基本原理和主要功能。其主要原则是不依赖继电保护装置独立判断各种故障类型,按预定的控制策略表采取切负荷、切机或快关汽门等措施。在此基础上,对系统网络化的硬件平台、通用的策略表查询软件和成熟的继电保护故障判别算法等关键技术的实现特点进行了说明。经过方案设计、工程试验和现场安装调试,黑龙江500kV电网安全稳定控制系统已正式投入运行。