Research on Optical Fiber Transport Network Topology Optimization Supporting Smart Grid Core Services

2010 ◽  
Author(s):  
Xin Miao ◽  
Xi Chen
Keyword(s):  
Topology Optimization ◽  
Smart Grid ◽  
Optical Fiber ◽  
Network Topology ◽  
Transport Network ◽  
Network Topology Optimization ◽  
Fiber Transport

Toward Online Control of Local Bifurcation in Power Systems via Network Topology Optimization

2015 ◽  
Vol 25 (12) ◽  
pp. 1550167
Author(s):  
Lei Wang ◽  
Hsiao-Dong Chiang
Keyword(s):  
Topology Optimization ◽  
Power Systems ◽  
Transmission Line ◽  
Network Topology ◽  
Online Control ◽  
Power Grids ◽  
Local Bifurcations ◽  
Network Topology Optimization ◽  
Online Methods ◽  
The Impact

This paper presents online methods for controlling local bifurcations of power grids with the goal of increasing bifurcation values (i.e. increasing load margins) via network topology optimization, a low-cost control. In other words, this paper presents online methods for increasing power transfer capability subject to static stability limit via switching transmission line out/in (i.e. disconnecting a transmission line or connecting a transmission line). To illustrate the impact of network topology on local bifurcations, two common local bifurcations, i.e. saddle-node bifurcation and structure-induced bifurcation on small power grids with different network topologies are shown. A three-stage online control methodology of local bifurcations via network topology optimization is presented to delay local bifurcations of power grids. Online methods must meet the challenging requirements of online applications such as the speed requirement (in the order of minutes), accuracy requirement and robustness requirement. The effectiveness of the three-stage methodology for online applications is demonstrated on the IEEE 118-bus and a 1648-bus practical power systems.

scholarly journals Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Maoqing Zhang ◽  
Lei Wang ◽  
Zhihua Cui ◽  
Jiangshan Liu ◽  
Dong Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Network Topology ◽  
Optimization Problems ◽  
Selection Strategy ◽  
Nsga Ii ◽  
Comparison Results ◽  
Practical Engineering ◽  
Network Topology Optimization ◽  
Nondominated Sorting

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.

scholarly journals Network topology optimization by turning non-scale-free networks into scale-free networks using nonlinear preferential rewiring method

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771878447 ◽  
Author(s):  
Feng Su ◽  
Peijiang Yuan ◽  
Yuanwei Liu ◽  
Shuangqian Cao
Keyword(s):  
Topology Optimization ◽  
Network Topology ◽  
Random Networks ◽  
Practical Application ◽  
Performance Simulation ◽  
Scale Free ◽  
Scale Free Networks ◽  
Network Topologies ◽  
Network Topology Optimization ◽  
Rigorous Mathematical Model

In practical application, the generation and evolution of many real networks always do not follow rigorous mathematical model, making network topology optimization a great challenge in the field of complex networks. In this research, we optimize the topology of non-scale-free networks by turning it into scale-free networks using a nonlinear preferential rewiring method. For different kinds of original networks generated by Watts and Strogatz model, we systematically demonstrate the optimization process and the modified networks to verify the performance of nonlinear preferential rewiring. We conduct further researches to explore the effect of nonlinear preferential rewiring’s parameters on performance. Simulation results show that various non-scale-free networks with different network topologies generated by WS model, including random networks and various networks between regular and random, are turned into scale-free networks perfectly by nonlinear preferential rewiring method.

scholarly journals AI-based network topology optimization system

2021 ◽  
Vol 2 (4) ◽  
pp. 81-90
Author(s):  
Han Zengfu ◽  
Kong Jiankun ◽  
Wang Zhiguo ◽  
Zhang Yiwei ◽  
Liu Ke ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Resource Utilization ◽  
Network Topology ◽  
Model Building ◽  
Short Term Memory ◽  
Capacity Utilization ◽  
Optimization Strategy ◽  
Edge Deletion ◽  
Optimization System ◽  
Network Topology Optimization

Existing network topology planning does not fully consider the increasing network traffic and problem of uneven link capacity utilization, resulting in lower resource utilization and unnecessary investments in network construction. The AI-based network topology optimization system introduced in this paper builds a Long Short-Term Memory (LSTM) model for time series traffic forecasting, which uses NetworkX, a Python library, for graph analysis, dynamically optimizes the network topology by edge deletion or addition based on traffic over nodes, and ensures network load balancing when node traffic increases, mainly introducing the LSTM forecasting model building process, parameter optimization strategy, and network topology optimization in some detail. As it effectively enhances resource utilization, this system is vital to the optimization of complex network topology. The end of this paper looks forward to the future development of artificial intelligence, and suggests the possibility of how to cooperate with operator networks and how to establish cross-border ecological development.

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