A recursive technique for tracking the feasible parameter set in bounded error estimation

In this paper, a new recursive algorithm is proposed for tracking parameter changes of a time‐varying linear system. Since a bounded error approach is adopted for both modeling the measurement noise and the parameter change process, the problem addressed amounts to the design of a procedure for updating an estimate of the feasible parameter set. The approximating regions considered are in the form of outbounding orthotopes. The novelty of the approach lies in the use of a selection technique which keeps track only of a special subset of the constraints defining the feasible set. These inequalities represent the binding constraints of suitable linear programs of limited size. The devised algorithm is tested on several numerical examples, showing remarkable performance both in computational burden, which is comparable to that of classical recursive estimation algorithms like recursive least squares (RLS) and quality of the set estimate as compared to alternative techniques available in the literature.     

Adaptive performance/ robust control design using orthotopic set membership identification for low-dimensional systems

The adaptive robust control design problem for low-dimensional systems is addressed in this paper. The proposed scheme consists of an orthotopic set membership identifier, a robust stabilizing controller and a pole placement controller. The identifier provides the minimum (volume-wise) orthotopic region of parameters which is consistent with the data and model structure. Based on this orthotope, the robust controller computes the feasible set of stabilizing controller gains. Subsequently a pole assignment controller selects a subset from the stabilizing gains in order to place the closed-loop poles deeper inside the unit disc. Simulation studies are offered to validate the efficiency of the proposed method. © 1997 John Wiley & Sons, Ltd.     

Simultaneous input and parameter estimation with input observers and set‐membership parameter bounding: theory and an automotive application

The paper addresses an on‐line, simultaneous input and parameter estimation problem for a first‐order system affected by measurement noise. This problem is motivated by practical applications in the area of engine control. Our approach combines an input observer for the unknown input with a set‐membership algorithm to estimate the parameter. The set‐membership algorithm takes advantage of a priori available information such as (i) known bounds on the unknown input, measurement noise and time rate of change of the unknown input; (ii) the form of the input observer in which the unknown parameter affects only the observer output; and (iii) the input observer error bounds for the case when the parameter is known exactly. The asymptotic properties of the algorithm as the observer gain increases are delineated. It is shown that for accurate estimation the unknown input needs to approach the known bounds a sufficient number of times (these time instants need not be known). Powertrain control applications are discussed and a simulation example based on application to engine control is reported. A generalization of the basic ideas to higher order systems is also elaborated. Copyright © 2006 John Wiley & Sons, Ltd.     

A set‐membership approach to integrated trade‐off design of robust fault detection system

In order to improve fault detection (FD) performance, integrated design of residual generation and evaluation is investigated in this paper for trade‐offs between fault detection rate and false alarm rate (FAR). A set‐membership approach is proposed in residual evaluation by adopting a threshold ellipsoid, which enables more design freedom than a conventional threshold value. With the set‐based definitions of fault detection rate and FAR, the integrated design problem is formulated by maximizing the FD performance under a predefined FAR. The joint optimal selection of a residual generator and a threshold ellipsoid is equivalently transformed into a simplified optimization problem of determining an optimal threshold ellipsoid for any given residual generator. A suboptimal solution for the set‐membership‐based integrated FD system design is obtained based on approximated computation of the FD performance. Monte Carlo simulations show the performance improvement of the proposed method compared with an existing integrated design method. Copyright © 2016 John Wiley & Sons, Ltd.     

定制化求解机组组合混合整数线性规划模型的固定—推断法

为了突破机组组合算法的自主可控问题,基于开源混合整数线性规划求解器CBC,提出一种快速获取机组组合问题可行解的固定—推断法。首先将机组组合模型转换为推断标准模型,然后按重要性对所有整数变量进行排序。并利用约束违反函数依次确定整数变量的值,实现整数变量的固定,利用约束关系推断出与其相关的整数变量值。最后经过多轮的固定—推断可以实现所有整数变量的取值,从而求解一个线性规划问题即可得到各机组的出力。仿真结果表明,所述算法能有效求解大规模机组组合问题,可在更短时间内获取质量较好的可行解。与CBC求解器结合,能显著提升CBC求解器对于机组组合问题的求解效率。此外,所述算法还具备在其他求解器上进行定制的潜力。     

Bias compensation‐based parameter estimation for output error moving average systems

Identification problems of output error models with moving average noises are considered in this paper. The least‐squares‐based parameter estimation is biased under the colored noises in outputs. Firstly, a bias compensation term is formulated to achieve the bias‐eliminated estimates of the system parameters. Secondly, the bias compensation term is determined by the unknown variance of the noise and the unknown noise model, thus based on the hierarchical identification principle, an unbiased parameter estimation is obtained by interactively estimating noise variance and noise parameters. Finally, the estimated bias compensation term is added to the biased parameter estimates. The simulation examples confirm the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

Robustness of convergence in finite time for linear programming neural networks

A recent work has introduced a class of neural networks for solving linear programming problems, where all trajectories converge toward the global optimal solution in finite time. In this paper, it is shown that global convergence in finite time is robust with respect to tolerances in the electronic implementation, and an estimate of the allowed perturbations preserving convergence is obtained. Copyright © 2006 John Wiley & Sons, Ltd.     

二层线性整数规划问题的一种全局优化方法及应用

在研究了整数规划连续途径的基础上，分别采用模拟退火算法和分枝定界法对一类二层线性整数规划的上级和下级问题进行了求解。在给出算法的同时，通过对价格控制问题算例的应用，提供了解决此类问题全局最优解的一种有效途径。     

负荷预测线性回归分析法的模糊改进

针对中长期电力负荷预测中观测数据及负荷变化规律的模糊性 ,采用模糊集合论中三角模糊数的概念 ,对传统的负荷线性回归预测模型作了改进。此方法通过建立具有模糊回归参数和模糊相关变量的回归模型 ,由模糊观测数据预测出未来负荷值。通过算例 ,验证了改进的负荷预测模型更加适用于中长期电力负荷预测     

下限分析中屈服面的最佳线性逼近问题

目前下限分析中往往采用内接正多边形来逼近屈服面以保证解的下限性质.从数值分析角度来看,采用其它拟合方式也能够从下方逼近极限荷载.本文研究了以下几种屈服面的多边形逼近方式:外切逼近、等面积逼近、最佳平方逼近、最优一致逼近、等周长逼近和内接逼近.计算分析表明:最优一致逼近在保证下限性质和求解精度的同时收敛更快,提高了求解效率,便于工程应用.     

基于启发式线性规划的大规模安全约束调度快速求解方法

提升大规模安全约束经济调度优化模型的求解性能是开展大电网跨省区电力电量全局优化平衡的前提与基础。首先分析问题的物理特性,通过并行计算求解不考虑机组爬坡约束的分时段约束松弛模型。基于对松弛解的分析获得可用于指导安全约束经济调度模型改进的有用信息,以约束剔除和约束增加的方式提出了基于启发式线性规划的大规模安全约束经济调度快速求解方法。将所提算法运用于新英格兰10机扩展系统和中国实际电网,验证了所提算法的正确性和有效性。     

A rough membership neural network approach for fault classification in transmission lines

Objective: This paper presents a new approach for fault classification in extra high voltage (EHV) transmission line using a rough membership neural network (RMNN) classifier.Methods: Wavelet transform is used for the decomposition of measured current signals and for extraction of ten significant time–frequency domain features (TFDF), as well as three distinctive time domain features (TDF) particularly in terms of getting better classification performance. After extracting useful features from the measured signals, a decision of fault type of a transmission line is carried out using ten RMNN classifiers. Furthermore, to reduce the training times of the neural network, the rough neurons are used as input layer neurons, and the fuzzy neurons are utilized in hidden and output layer in each RMNN. And the Back Propagation (BP) algorithm is employed for determining the optimal connection weights between neurons of the different layers in the RMNN.Results and Conclusions: To verify the effectiveness of the proposed scheme, extensive simulations have been carried out under different fault conditions with wide variations in fault type, fault resistance, fault location and fault inception angle. Simulations results show that the proposed scheme is faster and more accurate than the back-propagation neural network (BPNN), and it is proved to be a robust classifier for digital protection.     

A mathematical programming approach to service restoration for distribution systems

This paper aims to develop a suitable power system restoration method by using mathematical programming (MP). Generally, the problem of determining the target configuration can be formulated as a mixed integer programming (MIP) problem. Whether or not each branch is included in a target system is expressed as a 0–1 variable, while the other constraints such as the demand and supply balance conditions are formulated in terms of continuous variables. Although an MP‐based method can obtain an optimal solution under specified operational constraints, in the case of large‐scale problems the solution time often exceeds the computation time that is practically available due to combinatorial explosion. The object of this paper is to develop an efficient solution algorithm for service restoration in distribution systems. First, an MIP problem is solved to determine the sound network configuration that maximizes the available power to the out‐of‐service area, then an MIP problem is solved to determine the target configuration for the out‐of‐service network. The proposed method has been applied to distribution system restoration problems, and simulation results demonstrate its effectiveness. © 2001 Scripta Technica, Electr Eng Jpn, 135(4): 35–42, 2001     

Robust real-time parameter estimation for linear systems affected by external noises and uncertainties

A robust adaptive parameter estimation method, based on the application of a full-order filter capable of rejecting exogenous disturbances, is proposed in this article. A linear matrix inequality condition is proposed to synthesize the desired robust filter, assuming the presence of a known input control with constraints. The filter uses the output of the system to estimate the desired signal that will be employed in the adaptive estimation procedure and, to assure robustness to exogenous noise and unstructured uncertainties, the guaranteed cost is minimized in the synthesis condition. The filtered signals are then applied to an adaptive procedure to estimate the unknown system's internal parameters, which is also proposed in this article. It is shown that lower values for the guaranteed cost from the disturbance input to the error output of the filter imply more accurate estimations of the parameters. The efficiency of the proposed estimation technique is illustrated through a simulated model and a physical system has been considered to validate real-time estimation.     

A combined approach for the identification of continuous non‐linear systems

The identification of a non‐linear continuous output‐only system from a time series is considered for the case that the functional form of the model is not known beforehand. To estimate both functions and parameters, a combination of non‐parametric modelling based on non‐linear regression and parametric modelling based on a multiple shooting algorithm is proposed. This strategy to determine non‐linear differential equations is exemplified on experimental data from a chaotic circuit where an accurate reconstruction of the observed attractor is obtained. Copyright © 2003 John Wiley & Sons, Ltd.     

A unified set‐theoretic approach to the analysis of PWL resistive circuits and composite N‐ports

We consider the search for all DC solutions of resistive piecewise‐linear (PWL) circuits and the analysis of the characteristics of resistive PWL composite N‐ports. These problems are unified from a theoretical and operating point of view by introducing the so‐called N‐augmented circuit, obtained from the N‐port by closing its ports with N norators. Set‐theoretic approach is used to describe the properties of the N‐augmented circuits leading to the formulation of a general DC analysis algorithm, based on linear programming techniques. The examples at the end of the paper show some practical and efficient application of the general DC analysis algorithm. Copyright © 2010 John Wiley & Sons, Ltd.     

基于模糊隶属度及区间算术的电网电压优化新算法

传统无功优化由于寻优策略复杂、计算机时较长,目前尚较难应用于电网实时优化控制。基于最优电压模糊隶属度及区间潮流算法,提出了一种简单快速的寻优新思路。先由电网能提供的控制变量区间求解对应的电压区间,与电网安全约束电压区间求交后便得到既可行又能满足安全约束的电压区间。在此区间中找出隶属度最大的电压值,以此电压值为已知量求解反潮流,所得的控制变量解便为要求的最优控制变量组合。该方法与传统的智能算法比较,结果说明该方法的确优化速度快,适合作为省级电网的AVC系统电压优化的快速寻优方法。     

一种基于静态特性的直线感应电机参数辨识方法

直线感应电机(LIM)具有机械间隙大、定转子漏感差别明显、边缘效应影响等特点,准确测量电机参数难度较大.通常的电机空载试验参数测量方法在直线轨道上难于实施,特别是用于地铁和磁浮列车牵引的直线电机,由于运动位置重复性差,在线辨识实现困难.本文提出一种新的基于变频器驱动的直线感应电机等效电路参数辨识方法,利用不同频率时的电机推力、变频器输出电压和电流等静态堵转特性,结合最小二乘法推算出适合控制用的直线感应电机等效电路中的初级电阻、漏感、次级等效电阻和漏感参数.测量设备即为实际驱动电机的变频器.对一台基于直线感应电机牵引系统的测试结果表明了该方法的有效性.     

基于整数线性规划的配电网故障定位容错算法

为了提高配电网馈线故障定位的速度和容错能力,提出一种基于整数线性规划模型的配电网故障区段定位容错算法。将各馈线终端单元(Feeder Terminal Unit, FTU)的漏报、误报状况和馈线状态作为决策变量,基于最小故障诊断集理论,建立计及FTU漏报、误报信息的故障定位模型。针对逻辑运算在求解大规模配电网故障区段时建模复杂、算法受限的问题,构建基于代数关系运算的开关函数,并提出一种逻辑关系线性化策略将约束条件线性化。所建模型采用具有良好数值稳定性的常规优化方法进行决策,以保证解的全局最优性。仿真算例表明,所提方法定位准确高效、容错性强,在正确定位故障馈线区段的同时能获取具体节点的漏报、误报状况,适用于多节点过流信息发生畸变的大型配电网络。