Control synthesis of linear distributed parameter switched systems

The control synthesis for switched systems is extended to distributed parameter switched systems in Hilbert space. Based on semigroup and operator theory, by means of multiple Lyapunov method incorporated average dwell time approach, sufficient con- ditions are derived in terms of linear operator inequalities frame- work for distributed parameter switched systems. Being applied to one dimensional heat propagation switched systems, these lin- ear operator inequalities are reduced to linear matrix inequalities subsequently. In particular, the state feedback gain matrices and the switching law are designed, and the state decay estimate is explicitly given whose decay coefficient completely depends on the system＇s parameter and the boundary condition. Finally, two numerical examples are given to illustrate the proposed method.

Exponential stabilization of distributed parameter switched systems under dwell time constraints

The exponential stabilization problem for finite dimensional switched systems is extended to the infinite dimensional distributed parameter systems in the Hilbert space. Based on the semigroup theory, by applying the multiple Lyapunov function method, the exponential stabilization conditions are derived. These conditions are given in the form of linear operator inequalities where the decision variables are operators in the Hilbert space; while the stabilization properties depend on the switching rule. Being applied to the two-dimensional heat switched propagation equations with the Dirichlet boundary conditions, these linear operator inequalities are transformed into standard linear matrix inequalities. Finally, two examples are given to illustrate the effectiveness of the proposed results.

SPECTRUM DISTRIBUTION OF THE SEDOND ORDER GENERALIZED DISTRIBUTED PARAMETER SYSTEMS

Spectrum distribution of the second order generalized distributed parameter system was discussed via the functional analysis and operator theory in Hilbert space. The solutions of the problem and the constructive expression of the solutions are given by the generalized inverse one of bounded linear operator. This is theoretically important for studying the stabilization and asymptotic stability of the second order generalized distributed parameter system.

Linear-in-Parameter Models Based on Parsimonious Genetic Programming Algorithm and Its Application to Aero-Engine Start Modeling

A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.

Sliding mode control of uncertain systems with distributed time-delay: parameter-dependent Lyapunov functional approach

The robust stability and robust sliding mode control problems are studied for a class of linear distributed time-delay systems with polytopic-type uncertainties by applying the parameter-dependent Lyapunov functional approach combining with a new method of introducing some relaxation matrices and tuning parameters, which can be chosen properly to lead to a less conservative result. First, a sufficient condition is proposed for robust stability of the autonomic system; next, the sufficient conditions of the robust stabilization controller and the existence condition of sliding mode are developed. The results are given in terms of linear matrix inequalities （LMIs）, which can be solved via efficient interior-point algorithms. A numerical example is presented to illustrate the feasibility and advantages of the proposed design scheme.

Support vector classification algorithm based on variable parameter linear programming

To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed. In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model. The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given. An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.

Randomized Objective Function Linear Programming in Risk Management

The traditional linear programming model is deterministic. The way that uncertainty is handled is to compute the range of optimality. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each objective function coefficient, one at a time. This yields the range of optimality within which the decision variables remain constant. This sensitivity analysis is useful for helping the analyst get a sense for the problem. However, it is unrealistic because objective function coefficients tend not to stand still. They are typically profit contributions from products sold and are subject to randomly varying selling prices. In this paper, a realistic linear program is created for simultaneously randomizing the coefficients from any probability distribution. Furthermore, we present a novel approach for designing a copula of random objective function coefficients according to a specified rank correlation. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendency, spread, skewness and extreme values for the purpose of risk analysis. This enables risk analysis and business analytics, emerging topics in education and preparation for the knowledge economy.

Randomized Constraint Limit Linear Programming in Risk Management

Traditional linear program (LP) models are deterministic. The way that constraint limit uncertainty is handled is to compute the range of feasibility. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each constraint limit, one at a time. This yields the range of feasibility within which the solution remains feasible. This sensitivity analysis is useful for helping the analyst get a feel for the problem. However, it is unrealistic because some constraint limits can vary randomly. These are typically constraint limits based on expected inventory. Inventory may fall short if there are overdue deliveries, unplanned machine failure, spoilage, etc. A realistic LP is created for simultaneously randomizing the constraint limits from any probability distribution. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendencies, spread, skewness and extreme values for the purpose of risk analysis. The spreadsheet design presented is ideal for teaching Monte Carlo simulation and risk analysis to graduate students in business analytics with no specialized programming language requirement.

An Evolutionary Firefly Algorithm, Goal Programming Optimization Approach for Setting the Osmotic Dehydration Parameters of Papaya

An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established via a response surface technique with the resulting optimization formulation being a non-linear goal programming model. For optimization, a computationally efficient, FA-driven method is employed and the resulting solution is shown to be superior to those from previous approaches for determining the osmotic process parameters. The final component of this study provides a computational experimentation performed on the FA to illustrate the relative sensitivity of this evolutionary metaheuristic approach over a range of the two key parameters that most influence its running time-the number of iterations and the number of fireflies. This sensitivity analysis revealed that for intermediate-to-high values of either of these two key parameters, the FA would always determine overall optimal solutions, while lower values of either parameter would generate greater variability in solution quality. Since the running time complexity of the FA is polynomial in the number of fireflies but linear in the number of iterations, this experimentation shows that it is more computationally practical to run the FA using a “reasonably small” number of fireflies together with a relatively larger number of iterations than the converse.

Application of hydrometeorological coupled European flood forecasting operational real time system in Yellow River Basin

This study evaluated the application of the European flood forecasting operational real time system （EFFORTS） to the Yellow River. An automatic data pre-processing program was developed to provide real-time hydrometeorological data. Various GIS layers were collected and developed to meet the demands of the distributed hydrological model in the EFFORTS. The model parameters were calibrated and validated based on more than ten years of historical hydrometeorological data from the study area. The San-Hua Basin （from the Sanmenxia Reservoir to the Huayuankou Hydrological Station）, the most geographically important area of the Yellow River, was chosen as the study area. The analysis indicates that the EFFORTS enhances the work efficiency, extends the flood forecasting lead time, and attains an acceptable level of forecasting accuracy in the San-Hua Basin, with a mean deterministic coefficient at Huayuankou Station, the basin outlet, of 0.90 in calibration and 0.96 in validation. The analysis also shows that the ;simulation accuracy is better for the southern part than for the northern part of the San-Hua Basin. This implies that, along with the characteristics of the basin and the mechanisms of runoff generation of the hydrological model, the hydrometeorological data play an important role in simulation of hydrological behavior.

基于多周期性MILP模型的新型配电系统拓扑辨识方法

在新兴低成本、非接触式的电流传感器的基础上,提出了基于多周期性混合整数线性优化的拓扑辨识方法。基于支路电流绝对值误差建立了拓扑辨识的混合整数非线性优化(MINLP)模型,采用线性化方法将MINLP模型转化为混合整数线性优化(MILP)模型,并通过多周期性测量数据建立多周期性优化模型,从而减小伪测量误差的影响。此外,证明了支路电流传感器优化配置条件,以确保拓扑辨识的准确性。在IEEE-33节点系统的仿真测试结果表明,所提出的拓扑辨识方法拓扑辨识精度高,随多周期性场景的增加,拓扑辨识精度逐渐增加,且受伪测量误差的影响比受支路电流测量误差更大。

A Complex Algorithm for Solving a Kind of Stochastic Programming

Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper discusses a class of two-stage stochastic programming problems modeling with maximum minimum expectation compensation criterion (MaxEMin) under the probability distribution having linear partial information (LPI). In view of the nondifferentiability of this kind of stochastic programming modeling, an improved complex algorithm is designed and analyzed. This algorithm can effectively solve the nondifferentiable stochastic programming problem under LPI through the variable polyhedron iteration. The calculation and discussion of numerical examples show the effectiveness of the proposed algorithm.

Stability of Production Planning Problem with Fuzzy Parameters

The traditional production planning model based upon the famous linear programming formulation has been well known in the literature. The consideration of uncertainty in manufacturing systems supposes a great advance. Models for production planning which do not recognize the uncertainty can be expected to generate inferior planning decisions as compared to models that explicitly account the uncertainty. This paper deals with production planning problem with fuzzy parameters in both of the objective function and constraints. We have a planning problem to maximize revenues net of the production inventory and lost sales cost. The existing results concerning the qualitative and quantitative analysis of basic notions in parametric production planning problem with fuzzy parameters. These notions are the set of feasible parameters, the solvability set and the stability set of the first kind.

Optimal Set of Multiple Relays and Distributed Self-Selection in Cooperative Networks

In this paper we derive analytically the optimal set of relays for the maximal destination signal-to-noise ratio (SNR) in a two-hop amplify-and-forward cooperative network with frequency-selective fading channels. Simple rules are derived to determine the optimal relays from all available candidates. Our results show that a node either participates in relaying with full power or does not participate in relaying at all, and that a node is a valid relay if and only if its SNR is higher than the optimal destination SNR. In addition, we develop a simple distributed algorithm for each node to determine whether participating in relaying by comparing its own SNR with the broadcasted destination SNR. This algorithm has extremely low overhead, and is shown to converge to the optimal solution fast and exactly within a finite number of iterations. The extremely high efficiency makes it especially suitable to time-varying mobile networks.

基于线性化方法的交直流混合配电系统网架规划

随着配电系统中直流设备的大量接入,交直流混合配电系统得到越来越广泛的应用。为适应交直流混合配电系统中不同交直流(AC/DC)类型的负荷或电源的接入,提出一种新的交直流混合配电系统网架结构规划方法,该方法考虑了网架交直流配置的所有可能性。首先以二进制网络矩阵描述配电系统的网架结构;其次建立网架单层规划模型,将规划变量和运行变量同时进行优化以提升全局寻优能力;最后将单层规划模型进行线性化处理,转换为混合整数线性规划问题以提升求解效率。仿真结果表明,所提方法能够为配电系统中不同交直流类型的负荷和电源的接入提供最佳的网架配置方案,相比于传统的纯交流规划方案具有更好的经济性和供电能力,在算法上与经典遗传算法求解的双层规划方法相比,具有更好的全局寻优能力和计算效率。

Optimal configuration of distributed power flow controller to enhance system loadability via mixed integer linear programming

The increase of energy consumption has caused power systems to operate close to the limit of their capacity.The distributed power flow controller(DPFC),as a new member of distributed flexible AC transmission systems,is introduced to remove this barrier.This paper proposes an optimal DPFC configuration method to enhance system loadability considering economic performance based on mixed integer linear programming.The conflicting behavior of system loadability and DPFC investment is analyzed and optimal solutions are calculated.Thereafter,the fuzzy decision-making method is implemented for determining the most preferred solution.In the most preferred solution obtained,the investment of DPFCs is minimized to find the optimal number,locations and set points.Simulation results on the IEEERTS79 system demonstrate that the proposed method is effective and reasonable.

考虑需求响应和风电不确定性的电网调度

风电不确定性对电力系统的影响日益增加,文中提出一种考虑需求响应的电网源荷网经济调度方法。根据需求价格弹性理论和消费者心理学原理构建价格型需求响应模型,基于灵活补偿激励型需求响应充分考虑两类需求响应互补特性,增加两类需求响应对系统的备用约束。引入通用概率分布描述风电出力特性,以期望成本量化风电不确定性惩罚成本,并将目标函数中的非线性变量线性化处理,采用混合整数线性规划问题对模型进行求解。仿真结果表明,所提模型能够有效降低负荷的峰谷差,提高风电的消纳能力。

含分布式新能源和需求响应负荷的虚拟电厂定价机制及优化调度

面对电网中分布式风电、光伏发电以及柔性负荷的广泛接入,将这些资源通过虚拟电厂(VPP)进行聚合,采用合理的电价机制来引导用户参与需求响应,可以有效提升新能源的消纳能力并降低整体运营成本。传统的分时电价机制往往难以实现需求响应负荷与新能源出力的良好匹配,可能会导致需求响应的不合理或过度反应。为此,针对包括分布式风电、分布式光伏发电和柔性负荷的VPP,提出了一种基于新能源出力的VPP内部定价机制,并设置电力交易优先级来引导VPP内部各资源优化运行,以VPP的整体运营成本最小化为目标,构建了一个VPP优化调度的混合整数线性规划模型。基于内蒙古某地区的实际数据进行仿真计算,结果表明,与基于传统分时电价的优化结果相比,该方法能显著提高新能源的利用率并降低VPP的运行费用。

基于加权最小绝对值状态估计的有源配电网拓扑辨识方法

在配电系统状态估计中,一方面众多网络设备难以完全监控导致量测数据不足,另一方面,频繁的拓扑切换亟须开发高效的算法。因此,提出了一种配电网拓扑辨识的加权最小绝对值状态估计方法。首先,对传统基于线性规划的加权最小绝对值状态估计方法进行改进,以提高计算效率;其次,在重新制定的加权最小绝对值状态估计方法中加入补充变量和约束,以适应拓扑辨识问题,形成了一个基于混合整数线性规划的加权最小绝对值状态估计方法;最后,4个算例系统的仿真结果验证了所提方法在不同数量的实时测量、高伪测量误差、被坏数据破坏的测量以及未知支路状态场景下的优异性能。