基于稳定约束的电力系统稳定平衡解模型

为直接计算电力系统的静态稳定解,提出了基于稳定约束的稳定平衡解模型,并对该模型提出了一种直接求解稳定平衡解的新方法。通过稳定约束与电力系统典型的平衡方程结合,建立了稳定平衡解模型;稳定约束由非线性半光滑代数不等式构成,针对不同稳定类型所对应雅可比矩阵的特性,运用矩阵变换、谱函数性质等数学理论构造了不同的稳定约束表达式。利用牛顿光滑化方法将该模型转换为光滑方程,从而解决了稳定平衡解模型的数值求解问题。稳定平衡解模型将非稳定解排除在可行解之外,使求解过程得到简化,避免了计算稳定极限和求多个平衡解等复杂过程,而且通过参数调整能满足更高的稳定性能要求。通过典型的电力系统的数值计算,验证了所提方法的有效性。     

瞬时性故障最佳重合时刻的一种捕捉方法

在重合闸之前,按故障线路切除时的系统稳定平衡点和导纳矩阵来计算系统的暂态能量时,这一能量是守恒 的。文中通过分析计算指出,对于瞬时性故障,如果以重合闸成功后的稳定平衡点和导纳矩阵来计算重合闸 前系统的暂态能量,这一能量的大小 是变化的,这一变化的能量达到最小值的时刻就是瞬时性故障重合闸的 最佳时刻。     

时滞LPV离散系统的稳定新准则及控制器设计

针对一类具有状态时滞的线性参数变化离散时间系统，提出了一种新的依赖于参数的Lyapunov稳定条件。该准则通过引入一个附加矩阵解除了系统矩阵与依赖于参数的Lyapunov函数之间的耦合而更易于控制系统的分析与综合。在此基础上设计了此类系统的状态反馈控制器，并将控制器存在的充分条件转化为参数线性矩阵不等式的解存在条件。数值仿真验证了所提出算法的可行性。     

多时滞离散不确定系统的模糊输出反馈保性能控制

针对一类用T-S模糊模型描述的多时滞离散不确定系统,研究了其输出反馈保性能鲁棒控制问题.基于线性矩阵不等式方法,给出了系统模糊输出反馈保性能控制律存在的一个充分条件和性能上界,并证明了该条件等价于一组线性矩阵不等式的可行性问题.通过建立和求解一个凸优化问题,给出了系统设计次优保性能模糊输出反馈控制律的方法.最后通过算例验证了结果的有效性.     

频率相关转换矩阵的一种平滑算法

使用传统算法得到的转换矩阵中的元素存在突变点,会给有理函数近似带来很大困难。为此提出了一种新算法能够使转换矩阵成为频率的光滑函数;新算法先使用传统的算法计算出每个频率点的转换矩阵,然后再通过一个平滑算法消除转换矩阵中由传统算法产生的突变点。在实现新算法的基础上,通过一个双回三相不换位输电线路的算例将新算法与Newton-Raphson算法进行了比较。实验结果和理论分析表明新算法具有更高的效率和更好的追踪能力。     

基于非对称模型的发电机转子-轴承系统动力特性分析

该文在旋转坐标下建立了发电机非对称转子 -轴承系统的数学模型 ,推导了非对称转子、轮盘和轴承的传递矩阵。针对传统传递矩阵法存在的对多自由度非对称转子 -轴承系统的数值不稳定问题 ,该文在构造系统非对称部件的传递矩阵时 ,引入了Riccati变换 ,使之适合于复杂实际非对称转子动力特性的求解。将这种方法应用于某 6 5 0MW核电发电机转子 轴承系统的动力特性计算 ,计算结果与实验结果吻合的较好 ,证明该方法能解决传统方法不能解决的实际非对称转子 轴承系统动力特性 (临界转速、不平衡响应和稳定性等 )的计算问题 ,为非对称电机转子的设计提供了更准确的计算方法。     

时滞不确定奇异系统的鲁棒H∞控制

针对带时滞的奇异系统,研究基于状态反馈的鲁棒H∞问题,系统的状态矩阵、状态滞后矩阵和输入矩阵均含有范数有界不确定项。采用Lyapunov泛函方法,给出了带时滞的不确定奇异系统鲁棒渐近稳定且具有H∞范数界γ充分条件;利用含等式约束的线性矩阵不等式（LMI）方法,给出了此类系统控制存在状态反馈的充分条件,该条件等价于严格线性矩阵不等式可解性问题,利用严格LMI的可行解,得到控制器矩阵的参数化表示。该鲁棒控制器的渐进稳定性条件宽松,克服了时滞奇异系统稳定性问题条件中的等式约束,控制器求解算法可行、高效,并通过一个数值实例说明了该方法的有效性。     

遗传算法在过程信号的盲分离中的应用

盲分离神经网络算法存在着容易陷入局部极小点、收敛速度慢的缺点。提出采用遗传算法优化盲分离神经网络权值的初值,与遗传算法结合形成GA-HJNN算法,可迅速得到最佳盲分离神经网络的权值矩阵,实现对过程信号的去噪。通过实验对两种算法进行了比较。     

基于非对称神经网络结构的电站锅炉智能燃烧控制模型

针对目前国内电站锅炉燃烧建模存在煤质与负荷波动频繁、测点精度有限、设备运行组合多变等产生的问题,提出了电站锅炉燃烧的非对称神经网络建模方法。将锅炉模型的输入按照实际物理规律的关联关系设计网络结构组合,去掉关联性较弱的联系,从而使模型天然体现一定的锅炉燃烧规律,实现了不同燃烧器出力分配下的单一网络建模,提高了学习训练效率,并大幅降低了模型所需样本数量。将经典对称神经网络模型和非对称神经网络模型进行对比训练,结果表明本文提出的非对称神经网络建模方法检验正确率高。将其应用于某超临界660 MW机组的燃烧优化控制,锅炉效率平均可提高0.25%。     

基于遗传优化BP网络的振动故障诊断

为克服传统BP神经网络存在着容易陷入局部极小点、对初值要求高的缺点,采用遗传算法对BP神经网络的初值空间进行多点遗传优化,得到最佳初始权值矩阵,在此基础上按误差前向反馈算法沿负梯度搜索进行网络学习;同时提出了一种用于BP神经网络遗传优化的染色体浮点编码方法,并描述了作用于染色体上的遗传操作算法。仿真研究表明:遗传BP神经网络的收敛和诊断能力优于传统BP神经网络,可有效运用到汽轮发电机组振动故障诊断中。     

Equilibrium analysis of non-symmetric CNNs

Two useful results concerning the equilibrium analysis of non-symmetric cellular neural networks (CNNs) are presented. First a new sufficient condition ensuring the existence of a stable equilibrium point in the total saturation region is given. Then another condition which guarantees the uniqueness and global asymptotic stability of the equilibrium point is obtained.     

Absolute stability of a class of neural networks with unbounded delay

In this paper, the existence and uniqueness of the equilibrium point and absolute stability of a class of neural networks with partially Lipschitz continue activation functions are investigated. The neural networks contain both variable and unbounded delays. Using the matrix property, the necessary and sufficient condition for the existence and uniqueness of the equilibrium point of the neural networks is obtained. By constructing proper vector Liapunov functions and non‐linear integro‐differential inequalities involving both variable delays and unbounded delay, the sufficient conditions for absolute stability (global asymptotic stability) are obtained. Copyright © 2004 John Wiley & Sons, Ltd.     

On the dynamic behaviour of two-cell cellular neural networks

A complete study of non-linear differential equations describing two-cell cellular neural networks (CNNs) is presented. the stability properties are investigated and the domains of attraction of the stable fixed points are determined. Also, the conditions for the existence of periodic and homoclinic cycles are stated.     

Nonlinear Lipschitz measure and adaptive control for stability and synchronization in delayed inertial Cohen‐Grossberg–type neural networks

In this paper, without transforming the original inertial neural networks into the first‐order differential equation by some variable substitutions, time‐varying delays are introduced into inertial Cohen‐Grossberg–type networks and the existence, the uniqueness, and the asymptotic stability and synchronisation for the neural networks are investigated. Firstly, the existence of a unique equilibrium point is proved by using nonlinear Lipschitz measure method. Second, by finding a new Lyapunov‐Krasovskii functional, some sufficient conditions are derived to ensure the asymptotic stability, the asymptotic synchronization, and the asymptotic adaptive synchronization. The results of this paper are new and they complete previously known results. We illustrate the effectiveness of the approach through a few examples.     

Frequency domain criteria for cellular neural networks

A dynamical system is called globally asymptotically stable if it has a unique equilibrium point which attracts every trajectory in state space. As a consequence its steady state response is insensitive to initial conditions and then depends only on the input. In this paper some criteria are presented for the global asymptotic stability of cellular neural networks (CNNs), concerning both discrete-time and continuous-time dynamics. The proposed criteria represent necessary and sufficient conditions that can easily be checked by computing the discrete Fourier transform of the template elements. For this reason they have been called frequency domain stability criteria. These criteria provide milder constraints on the template coefficients than required in existing results for general recurrent neural network models. © 1997 by John Wiley & Sons, Ltd.     

Stability analysis of delayed fuzzy Cohen-Grossberg neural networks with discontinuous activations

In this article, we investigate the dynamical behavior of a class of delayed fuzzy Cohen-Grossberg neural networks (FCGNNs) with discontinuous activation functions subject to time delays and fuzzy terms. By using the inequality analysis technique and the M-matrix theory, sufficient and proper conditions are given in order to establish the existence, convergence, and global exponential stability of equilibrium point of the system. In particular, we discuss the impact of discontinuous neuron activations on the existence and exponential stability of equilibrium point for FCGNNs. Two numerical examples are provided to substantiate the theoretical results.     

Absolutely exponential stability in delayed cellular neural networks

In this paper, without assuming the boundedness, monotonicity and differentiability of the activation functions, we present a necessary and sufficient condition ensuring the existence and uniqueness of the equilibrium point of cellular neural network with fixed time delays (DCNNs). Using M‐matrix theory, Liapunov functionals and functions are constructed and employed to establish sufficient conditions for absolutely exponential stability of DCNNs. The results are applicable to DCNNs with both symmetric and non‐symmetric interconnection matrices, and globally Lipschitz continuous activation functions. Copyright © 2002 John Wiley & Sons, Ltd.     

Stability analysis of generalized cellular neural networks

A rather general class of neural networks, called generalized cellular neural networks (CNNs), is introduced. the new model covers most of the known neural network architectures, including cellular neural networks, Hopfield networks and multilayer perceptrons. Several sets of conditions ensuring the input-output stability and global asymptotic stability of generalized CNNs have been obtained. the conditions for the stability of individual cells are checked in the frequency domain, while the stability of the overall network is analysed in terms of the stability of individual cells and the connectivity characteristics. the results on the global asymptotic stability are useful for the design of a generalized CNN such that the orbit of each state converges to a globally asymptotically stable equilibrium point which depends only on the input and not on the initial state. Such a network defines an algebraic map from the space of external inputs to the space of steady state values of the outputs and hence can accomplish cognitive and computational tasks.     

Cellular neural network with trapezoidal activation function

This paper presents a cellular neural network (CNN) scheme employing a new non‐linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non‐separable data points and realize Boolean operations (including eXclusive OR) by using only a single‐layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright © 2005 John Wiley & Sons, Ltd.     

On global asymptotic stability for a class of delayed neural networks

This paper deals with the problem of stability analysis for a class of delayed neural networks described by nonlinear delay differential equations of the neutral type. A new and simple sufficient condition guaranteeing the existence, uniqueness and global asymptotic stability of an equilibrium point of such a kind of delayed neural networks is developed by the Lyapunov–Krasovskii method. The condition is expressed in terms of a linear matrix inequality, and thus can be checked easily by recently developed standard algorithms. When the stability condition is applied to the more commonly encountered delayed neural networks, it is shown that our result can be less conservative. Examples are provided to demonstrate the effectiveness of the proposed criteria. Copyright © 2011 John Wiley & Sons, Ltd.