国内配电网一般采用闭环设计,开环运行的供电方式,城市10 k V配电网络已形成了"手拉手"的环行供电网络,通过解合环操作可减少停电时间,供电可靠性也得到一定的提高,但依然存在短时停电问题,随着我国社会经济的快速发展,负荷...国内配电网一般采用闭环设计,开环运行的供电方式,城市10 k V配电网络已形成了"手拉手"的环行供电网络,通过解合环操作可减少停电时间,供电可靠性也得到一定的提高,但依然存在短时停电问题,随着我国社会经济的快速发展,负荷需求增长迅速,尤其是重要敏感用户对供电可靠性的要求越来越高,迫切需要进一步提供配网供电可靠性。文中对10k V配电网闭环运行模式进行研究分析,并提出一种可行的网架结构优化方案。展开更多
As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of c...As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.展开更多
Learning control for gradually varying references in iteration domain was considered in this research, and a composite iterative learning control strategy was proposed to enable a plant to track unknown iteration-depe...Learning control for gradually varying references in iteration domain was considered in this research, and a composite iterative learning control strategy was proposed to enable a plant to track unknown iteration-dependent trajectories. Specifically, by decoupling the current reference into the desired trajectory of the last trial and a disturbance signal with small magnitude, the learning and feedback parts were designed respectively to ensure fine tracking performance. After some theoretical analysis, the judging condition on whether the composite iterative learning control approach achieves better control results than pure feedback contro! was obtained for varying references. The convergence property of the closed-loop system was rigorously studied and the saturation problem was also addressed in the controller. The designed composite iterative learning control strategy is successfully employed in an atomic force microscope system, with both simulation and experimental results clearly demonstrating its superior performance.展开更多
文摘国内配电网一般采用闭环设计,开环运行的供电方式,城市10 k V配电网络已形成了"手拉手"的环行供电网络,通过解合环操作可减少停电时间,供电可靠性也得到一定的提高,但依然存在短时停电问题,随着我国社会经济的快速发展,负荷需求增长迅速,尤其是重要敏感用户对供电可靠性的要求越来越高,迫切需要进一步提供配网供电可靠性。文中对10k V配电网闭环运行模式进行研究分析,并提出一种可行的网架结构优化方案。
基金Project(2011ZK2030)supported by the Soft Science Research Plan of Hunan Province,ChinaProject(2010ZDB42)supported by the Social Science Foundation of Hunan Province,China+1 种基金Projects(09A048,11B070)supported by the Science Research Foundation of Education Bureau of Hunan Province,ChinaProjects(2010GK3036,2011FJ6049)supported by the Science and Technology Plan of Hunan Province,China
文摘As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.
基金Projects(61127006,61325017)supported by the National Natural Science Foundation of China
文摘Learning control for gradually varying references in iteration domain was considered in this research, and a composite iterative learning control strategy was proposed to enable a plant to track unknown iteration-dependent trajectories. Specifically, by decoupling the current reference into the desired trajectory of the last trial and a disturbance signal with small magnitude, the learning and feedback parts were designed respectively to ensure fine tracking performance. After some theoretical analysis, the judging condition on whether the composite iterative learning control approach achieves better control results than pure feedback contro! was obtained for varying references. The convergence property of the closed-loop system was rigorously studied and the saturation problem was also addressed in the controller. The designed composite iterative learning control strategy is successfully employed in an atomic force microscope system, with both simulation and experimental results clearly demonstrating its superior performance.
