Center-configuration selection technique for the reconfigurable modular robot

The reconfigurable modular robot has an enormous amount of configurations to adapt to various environments and tasks. It greatly increases the complexity of configuration research in that the possible configuration number of the reconfigurable modular robot grows exponentially with the increase of module number. Being the initial configuration or the basic configuration of the reconfigurable robot, the center-configuration plays a crucial role in system’s actual applications. In this paper, a novel center-configuration selection technique has been proposed for reconfigurable modular robots. Based on the similarities between configurations’ transformation and graph theory, configuration network has been applied in the modeling and analyzing of these configurations. Configuration adjacency matrix, reconfirmation cost matrix, and center-configuration coefficient have been defined for the configuration network correspondingly. Being similar to the center-location problem, the center configuration has been selected according to the largest center-configuration coefficient. As an example of the reconfigurable robotic system, AMOEBA-I, a three-module reconfigurable robot with nine configurations which was developed in Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), has been introduced briefly. According to the numerical simulation result, the center-configuration coefficients for these nine configurations have been calculated and compared to validate this technique. Lastly, a center-configuration selection example is provided with consideration of the adjacent configurations. The center-configuration selection technique proposed in this paper is also available to other reconfigurable modular robots. Supported in part by the National High-Technology 863 Program (Grant No. 2001AA422360), the Chinese Academy of Sciences Advanced Manufacturing Technology R&D Base Fund (Grant Nos. A050104 and F050108), and the GUCAS-BHP Billiton Scholarship     

面向Web服务重组的平衡多目标遗传算法研究

基于Web服务的电子商务和电子政务已成为未来发展的趋势.由于一个Web服务可能由多个子服务组成,而这些子服务和用户的请求都可能发生变化,因此Web服务的重组越来越普遍,也越来越受到重视.目前相关研究集中在Web服务的描述及管理架构问题上,缺乏对服务重组的理论基础研究.平衡多目标遗传算法(BMGA)在经典GA基础上作了改进,使各方向平衡进化,非主要进化方向也不会妨碍主要进化方向的优化.BMGA被应用到Web服务重组中,试验结果证明了该算法的可行性和先进性.     

基于EJB分布式应用的动态配置

分布式应用程序在配置时需要引入传输和同步到应用程序代码的高级特性,EJB与CCM组件模型允许程序员声明其服务必须添加到组件的配置描述。然而,这种方法不允许将新服务动态地添加到整体组件。文中提出一种方法允许应用程序在运行时将新服务添加到整体组件。当一个新服务添加到一个组件时,已经运行并存在的服务组件通过平台进行动态地管理。     

基于蚁群算法的配电网网络重构

针对配电网网络重构问题,在考虑配电网电压稳定的前提下,提出了降低配电网网损的目标函数,利用蚁群算法正反馈的特性,将其应用于配电网重构中,并设置中心控制蚂蚁搜索当前最优解作为各条边信息素更新依据。为满足配电网辐射状结构要求,结合P rim算法,使蚂蚁一次遍历对应一个辐射网形,即一个有效的开关组合,大幅度缩小了问题的解空间。实例证明采用的蚁群算法可以得到较文献[1]网损更小的配电网重构方案,且重构后的系统在处理负荷增大问题上较重构前系统有更好的调控能力,系统稳定性得到提高。     

遗传粒子群混合算法在配电网络重构多目标优化中的应用

针对配电网络重构多为单一性能最优重构的问题,文章提出了使配电网线损、负荷均衡、供电电压质量最佳的多目标配网优化模型。结合GA中的进化思想和粒子群算法(PSO)中的群体智能技术,采用遗传粒子群混合算法寻优,通过随机权重方法来获得目标是Pareto前沿面的可搜索方向,体现出较GA和PSO更好的寻优性能。寻优过程中,部分个体以PSO方法迭代,其它个体进行GA中的选择、交叉和变异操作,整个群体信息共享,同时采用自适应参数机制和优胜劣汰的思想进化。在此基础上制定的配网优化方案能够在保证配网呈辐射状、满足馈线热容、电压降落要求和变压器容量等的前提下,最大限度地提高配电系统安全性和经济性。算例表明该算法在求解性能和效率两方面都有比较显著的优势。     

基于蚁群最优算法的配电网重构

在电力系统中配电网的重构是配电网降低网络损耗的重要途径。但是由于配网本身的结构,其重构是一个非常复杂的大规模组合优化问题。提出了基于蚁群最优的算法来求解故障情况下的配电网络重构问题,通过改变开关的闭合状态来改变网络的拓扑结构,以达到网络损失最小。蚁群最优算法(Ant colony Optimization,简称 ACO 算法)是模仿现实中的蚂蚁寻食的过程来求解配电网的重构问题。这种方法的主要特征是正反馈、分布式计算以及富有建设姓的贪婪启发式搜索的运用。该算法不依赖各种初始参数,不易陷于局部最优,为了验证本文提出的算法的可行性和有效性,我们研究了一个算例。结果表明,蚁群算法与现有各种方法相比,具有适应性好、计算效率高、优化效果好的优点。     

Reconfiguration and DG Sizing and Placement Using Improved Shuffled Frog Leaping Algorithm

Abstract

In this paper is proposed a reconfiguration methodology with the presence of Distributed Generation (DG), aimed at achieving the minimum power loss, minimum number of switching operation and minimum deviation of bus voltage while satisfying all constraints using improved shuffled frog leaping algorithm (ISFLA).The performance of the proposed method is examined on 33 and 69 bus IEEE test distribution systems. The ISFLA performance is evaluated with the well-known algorithm including of harmony search algorithm (HSA), refined genetic algorithm (GRA), particle swarm optimization (PSO), differential evolutionary (DE) and conventional SFLA. Simulation results showed that the total power loss and voltage bus minimum in primary distribution network can be reduced significantly. Also the results in different scenarios are showed that the simultaneous reconfiguration and DG placement method is better in less losses and also in more minimum voltage. Moreover, the ISFLA superiority is proved in comparison with the HAS, GRA, PSO, DE, and SFLA in view of more convergence speed and accuracy and also converges in less number iteration. Also, the performance of the proposed method is favorable compared to previous studies.     

A Coordinated Framework of DG Allocation and Operating Strategy in Distribution System for Configuration Management under Varying Loading Patterns

Abstract

The distributed generation (DG) planning with the varying pattern of the practical load is difficult as it calls for the frequent changes in DG size and system configuration, which is neither feasible nor permissible. Rather such a DG size and a configuration, which can be utilized over a wider load pattern, are more acceptable. This work presents a coordinated approach for DG planning and system reconfiguration. While to operate a particular DG size and the system configuration over a wide range of loading pattern, the configurations are ranked under different probabilistic loading patterns. Based upon the ranking of the new configuration, the energy performance of the coordinated planning is evaluated. Further, the observations from coordinated planning are imposed on coordinated operation using harmony search algorithm (HSA). The proposed approach is tested for single as well as multi-objectives on a 33-node system. A significant improvement in the computational efforts and energy performance of the resulting configuration have been observed where losses have reduced to 81.11 and 53.77?kW with single DG and multi-DG allocation respectively.     

Computational comparison of two formulations for dynamic supply chain reconfiguration with capacity expansion and contraction

The strategic dynamic supply chain reconfiguration (DSCR) problem is to prescribe the location and capacity of each facility, select links used for transportation, and plan material flows through the supply chain, including production, inventory, backorder, and outsourcing levels. The objective is to minimize total cost. The network must be dynamically reconfigured (i.e., by opening facilities, expanding and/or contracting their capacities, and closing facilities) over time to accommodate changing trends in demand and/or costs. The problem involves a multi-period, multi-product, multi-echelon supply chain. Research objectives of this paper are a traditional formulation and a network-based model of the DSCR problem; tests to promote intuitive interpretation of our models; tests to identify computational characteristics of each model to determine if one offers superior solvability; and tests to identify sensitivity of run time relative to primary parameters.     

Control reconfiguration after actuator failures by Markov parameter matching

Two novel linear control reconfiguration methods for plants subject to actuator failures are described. The common idea is to place a reconfiguration block between the faulty plant and the nominal controller in order to re-route the signals around the broken actuator. The first method uses a computationally simple static reconfiguration block. It recovers the nominal plant input/output-behaviour by assigning the faulty plant the same Markov parameters as the faultless plant. The second method concerns the design of the feedforward part in the virtual actuator using the idea and results of the first approach. The virtual actuator is a dynamical reconfiguration block. Existence conditions and solution algorithms are provided, and it is shown that both approaches guarantee the closed-loop stability if the existence conditions are met. An experimental study demonstrates the practical usability of the proposed approaches.