基于多层次灰色关联分析的创新合作网络节点评估研究

基于网络中心性指标的分析,有效融合网络度中心度、网络中介中心度、网络接近中心度、网络特征向量中心度四种中心性指标的优点,构建多层次灰色关联分析的节点综合评估模型,对网络中节点的重要性进行综合评估,对网络中核心节点予以判定.结合新一代信息技术领域专利合作网络的数据进行分析,结果表明模型在节点重要性排序结果上比以往的方法更科学,不同节点重要性区分度也更高.     

基于改进节点重要度引力模型的航路网拥堵节点识别

航路网络存在一些关键航路点对系统整体具有重要控制作用,而有效辨识这些重要节点对缓解空中交通拥堵,提高航路运行效率具有极大意义.以复杂网络理论为基础,从网络视角结合中心度及节点服务能力通过改进传统引力模型进行节点重要度评估;然后基于改进节点重要度引力模型进行航路网络拥堵节点识别仿真,并将结果与传统介数法进行对比验证.研究表明:改进的引力模型识别的重要节点,不仅具有较大的中心度,且较多位于航路网络流量较大位置;基于引力模型识别航路网络节点重要度的准确率更高,在提高网络通行率基础上更及时地识别航路网络易拥堵节点,对预防节点失效、减少航路网络拥堵具有积极作用.     

基于TOPSIS模型的机场网络节点重要度评估

在机场网络中单个机场节点的失效往往会对其他的节点产生影响,特别是关键节点的失效会波及整个网络.准确客观的识别重要节点机场关乎整个机场网络的安全运营.本文分析了机场网络拓扑特性中的度、集聚系数和接近度指标,考虑了机场旅客吞吐量和所在城市人口等交通经济特性指标,使用熵权法确定权重的基础上,应用TOPSIS法构建综合评价体系模型,最后以华东地区机场网络为例进行节点重要度排序.结果表明与单一指标的评估结果相比,该方法更加全面客观的确定不同属性指标的权重,避免了不同指标取值的差异性,使评价更加全面,更符合机场网络实际运营情况.     

基于网络形态和节点中心度的项目计划问题研究

为深入挖掘项目网络拓扑结构中所蕴含的信息,探索求解项目网络计划问题的新算法,以弥补当前不确定性网络计划问题求解方法的不足,将网络科学领域中度量节点中心度和网络形态特征的指标运用于项目活动网络,构建了基于时长的介数中心度、基于位置的介数中心度等指标来度量项目网络节点中心度,用平均度、度分布熵、平均距离等指标来度量项目网络的形态特征.研究发现:两种介数中心度指标都与活动关键性正相关,可据此来预估项目活动的关键性;活动平均度与求解所需的时间正相关;顺序强度、活动平均距离和平均时长与项目总工期正相关.     

基于理想灰关联距离的课程评估方法

综合灰色系统理论、理想解法和欧氏距离,提出了一种新的基于理想关联距离度的课程评估方法,给出了建立评估模型的基本步骤.定量处理的指标,经过理想化、标准化后,定义关联数,由此计算关联距离度.通过灰色关联距离度,建立了一种接近最优方案远离最差方案的评估模型.并通过学院近期的课程评估实例分析,验证了该方法的准确性和可行性.     

基于状态势博弈的电力系统分布式经济调度方法

未来电网中出现的主要变革之一在于电力系统电源结构的变化,分布式能源发电设备的广泛接入给传统调度与控制框架带来了严峻挑战.电力系统尤其是主动配电网中,调度控制体系的去中心化成为一种趋势.状态势博弈理论为电力系统分布式调度与控制体系的构建提供了一个良好框架.文章基于状态势博弈理论,给出了分布式电力系统经济调度的一般设计方法.该方法能够兼顾全网功率平衡约束与网络传输功率限制,同时实现主体间通信量极小化,因而具有较强的算法鲁棒性与实际应用价值.通过IEEE 9节点标准系统的仿真分析验证了所提方法的有效性及正确性.     

基于连通度的多跳传输路由算法

针对由各种功能不同的传感器构成的无线传感器网络,提出了一种基于连通度的多跳传输的路由协议(MHTA),该算法根据网络中节点能量分布的不同,引入权重因子作为调节参数进行簇头选举,选举过程中综合考虑了各节点消耗能量,与基站的邻近程度和周围邻节点的数目.为了减少在数据传输过程中消耗的能量,提出了一种簇内多跳和簇间多跳相结合进行数据传输的方法.模拟实验结果证明:该算法能够降低簇头能量消耗,均衡网络负载,从而延长网络生存时间.     

上海市公交超网络拓扑特征与鲁棒性分析

针对复杂公交网络从单一线路或站点角度分析的局限以及二分图中节点异质性,基于超网络理论多层次研究公交系统结构.将公交站点视为超边并将线路视为节点构建上海市公交超网络模型,分析网络节点度与节点超度、超边度与超边超度、平均路径长度以及聚类系数等拓扑特征,基于网络效率分析其鲁棒性.研究发现上海市公交超网络超边指标呈幂律分布,节点指标呈分段指数分布,有较小平均路径长度和较大聚类系数.公交超网络在受到随机攻击时鲁棒性较强而在受到基于节点度的蓄意攻击时鲁棒性最弱.对城市交通规划与设施管理具有指导意义.     

基于CCN节点内容流行规律的缓存路由策略

针对内容中心网络(CCN)中缓存路由策略的缓存可用性差、路径外缓存难以有效利用的问题,提出了一种动态缓存通告及节点内容流行规律的缓存路由策略.依据不同节点内容流行度进行差异化动态缓存通告,利用缓存通告信息及节点内容流行规律依概率进行缓存决策,增加高流行度内容的缓存概率及临近缓存的差异性;基于缓存通告信息建立局部吸引势场,并结合内容源建立全网吸引势场,实现内容请求的就近响应.仿真结果表明,在保证存储开销和传输开销的前提下,本策略可有效降低平均请求时延,提高缓存命中率.     

基于综合效益的城市地下物流网络设计与优化方法

针对城市地下物流系统(Underground Logistics System,ULS)的特征,对一系列ULS网络节点选址与优化问题进行了建模分析.1)从解决城市交通拥堵的角度出发,探讨了物流地上地下分配的三种方案,并建立了地下货运OD评价模型.2)综合权衡货运量与货源距离,基于改进的模糊C均值聚类确定ULS—级节点的选址和辐射范围.3)对每个一级区域构建了ULS二级节点选址优化模型,通过人工免疫算法搜索最少覆盖节点群及节点的最优归属.4)建立多目标ULS网络规划模型,结合Prim算法与Dijkstra算法实现货物地下运输路径的最优选择,并采用栅格覆盖的思路在节点服务范围内对ULS网络进行费用优化.5)提出ULS网络效能评估指标,设置中心节点以提高系统运输效率和抗风险能力.     

Solving a novel multiobjective placement problem of recloser and distributed generation sources in simultaneous mode by improved harmony search algorithm

This article deals with optimal placement of Distributed Generation (DG) sources and recloser in simultaneous mode and develops an improved harmony search (iHS) algorithm to solve it. For this, two important control parameters have been adjusted to reach better solution from simple HS algorithm to obtain better solution from simple HS algorithm. The proposed multiobjective function consists of two parts; first is improving reliability indices and second is minimizing power loss. The reliability indices have been selected based on satisfactory requirements of costumer and electric company as well as response to transient and permanent faults. Then, four reliability indices has been used in objective function; that is, system average interruption duration index (SAIDI), cost of energy not supplied, momentary average interruption frequency index, and system average interruption frequency index (SAIFI). Simulation has been performed on a practical distribution network in North West of Iran. Three scenarios have been introduced; that is, scenario (i) First, placement of DGs, and then recloser, scenario (ii) First, placement of recloser, and then DG, and scenario (iii) simultaneous placement of DG and recloser. Also, three cases are defined based on the number of used DG and recloser. Results of the proposed algorithm have been compared with related values of particle swarm optimization and simple HS algorithms. The core contribution of the presented study is introducing several novel indices to analyze and discuss the obtained results from simulation. © 2014 Wiley Periodicals, Inc. Complexity 21: 328–339, 2015     

An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability

Smart grid is referred to a modernized power grid which can mitigate fault detection and allow self‐healing of the system without the intervention of operators. This article proposes an innovative analytical formulation using Markov method to evaluate electric power distribution system reliability in smart grids, which incorporates the impact of smart monitoring on the overall system reliability. An accurate reliability model of the main network components and the communication infrastructure have been also considered in the methodology. The proposed approach was applied to a well‐known test bed (Roy Billinton Test System) and various reliability case studies with monitoring provision and monitoring deficiency are analyzed. This article involves the developing possibilities of communication technologies and next‐generation control systems of the entire smart network based on the real‐time monitoring and modern control system to achieve a reliable, economical, safe, and high efficiency of electricity. The implementations indicate that using an appropriate set of the smart grid monitoring devices for power system components can virtually influence all the reliability indices although the amount of improvement varies between techniques. The proposed approach determined that smart monitoring for which components of the electric power distribution systems are tailored and deduce to major economical benefits. The described approach also reveals which reliability indices drastically influenced using monitoring. © 2014 Wiley Periodicals, Inc. Complexity 21: 99–113, 2015     

Vulnerability of complex networks

We consider normalized average edge betweenness of a network as a metric of network vulnerability. We suggest that normalized average edge betweenness together with is relative difference when certain number of nodes and/or edges are removed from the network is a measure of network vulnerability, called vulnerability index. Vulnerability index is calculated for four synthetic networks: Erdős–Rényi (ER) random networks, Barabási–Albert (BA) model of scale-free networks, Watts–Strogatz (WS) model of small-world networks, and geometric random networks. Real-world networks for which vulnerability index is calculated include: two human brain networks, three urban networks, one collaboration network, and two power grid networks. We find that WS model of small-world networks and biological networks (human brain networks) are the most robust networks among all networks studied in the paper.     

An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation

This article proposes a new integrated diagnostic system for islanding detection by means of a neuro‐fuzzy approach. Islanding detection and prevention is a mandatory requirement for grid‐connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. Although passive schemes have a large non‐detection zone (NDZ), concern has been raised on active method due to its degrading power‐quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro‐fuzzy inference system) for islanding detection. This approach utilizes rate of change of frequency (ROCOF) at the target DG location and used as the input sets for a neuro‐fuzzy inference system for intelligent islanding detection. This approach utilizes the ANFIS as a machine learning technology and fuzzy clustering for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loading, switching operation, and network conditions. The proposed algorithm is compared with the widely used ROCOF relays and found working effectively in the situations where ROCOF fails. Simulation studies showed that the ANFIS‐based algorithm detects islanding situation accurate than other islanding detection algorithms. © 2014 Wiley Periodicals, Inc. Complexity 21: 10–20, 2015     

Study on economic withholding in electricity market of Zhejiang Province,China

Power suppliers with market power intend to bid high-price to obtain excessive profit when intensions in the balance of electricity supply and demand emerge. New index is thus necessary to be defined to assess the economic withholding behavior associated with both bidding prices and corresponding bidding capacity. Stages of low price section, economic withholding section and reasonable adventure section were classified in this paper. Based on bidding prices and capacity, absolute index and relative index were proposed to measure the economic withholding degree, with the former used for estimation of the total power system while the latter for comparisons amongst different units. A case study on Zhejiang electricity market indicated that these two proposed indices can accurately assess the economic withholding behavior in the electricity market. Finally, upper limit was suggested to be set for the relative index to suppress the behavior of excessive bidding prices in short of capacity.     

A unified network performance measure with importance identification and the ranking of network components

In this paper, we propose the first network performance measure that can be used to assess the efficiency of a network in the case of either fixed or elastic demands. Such a measure is needed for many different applications since only when the performance of a network can be quantifiably measured can the network be appropriately managed. Moreover, as we demonstrate, the proposed performance measure, which captures flow information and behavior, allows one to determine the criticality of various nodes (as well as links) through the identification of their importance and ranking. We present specific networks for which the performance/efficiency is computed along with the importance rankings of the nodes and links. The new measure can be applied to transportation networks, supply chains, financial networks, electric power generation and distribution networks as well as to the Internet and can be used to assess the vulnerability of a network to disruptions.     

Wavelet neural network based on islanding detection via inverter‐based DG

In this article, a passive neurowavelet based on islanding detection technique for grid‐connected inverter‐based distributed generation has been developed. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem‐free. Unintentional islanding is one of the encountered problems. Islanding is the situation where the distribution system containing both distributed generator and loads is separated from the main grid as a result of many reasons such as electrical faults and their subsequent switching incidents, equipment failures, or preplanned switching events like maintenance. The proposed method utilizes and combines wavelet analysis and artificial neural network to detect islanding. Discrete wavelet transform is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. Passive schemes have a large nondetection zone (NDZ) and concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The simulations results, performed by MATLAB/Simulink, shows that the proposed method has a small NDZ. Also, this method is capable of detecting islanding accurately within the minimum standard time. © 2014 Wiley Periodicals, Inc. Complexity 21: 309–324, 2015     

A network efficiency measure with application to critical infrastructure networks

In this paper, we demonstrate how a new network performance/efficiency measure, which captures demands, flows, costs, and behavior on networks, can be used to assess the importance of network components and their rankings. We provide new results regarding the measure, which we refer to as the Nagurney–Qiang measure, or, simply, the N–Q measure, and a previously proposed one, which did not explicitly consider demands and flows. We apply both measures to such critical infrastructure networks as transportation networks and the Internet and further explore the new measure through an application to an electric power generation and distribution network in the form of a supply chain. The Nagurney and Qiang network performance/efficiency measure that captures flows and behavior can identify which network components, that is, nodes and links, have the greatest impact in terms of their removal and, hence, are important from both vulnerability as well as security standpoints.     

基于复杂网络的并联式项目群结构脆弱性评估

运用复杂网络研究并联式项目群的结构脆弱性。将项目群中的任务用节点表示、任务之间的依赖关系用边表示,依据任务的工期确定点权、任务之间的依赖强度确定边权,则并联式项目群被抽象为一个有向加权网络。在分析并联式项目群网络拓扑特征的基础上,构建并联式项目群脆弱性评估模型,并对一个并联式项目群进行脆弱性评估。仿真结果表明该方法可以对并联式项目群脆弱性进行定量化评估、能够有效识别结构中的脆弱环节。本研究为保障项目群顺利实施、抵御风险提供了新的思路。