The Optimization Study about Fault Self-Healing Restoration of Power Distribution Network Based on Multi-Agent Technology

In order to quickly and accurately locate the fault location of the distribution network and increase the stability of the distribution network,a fault recovery method based on multi-objective optimization algorithm is proposed.The optimization of the power distribution network fault system based on multiagent technology realizes fast recovery of multi-objective fault,solve the problem of network learning and parameter adjustment in the later stage of particle swarm optimization algorithm falling into the local extreme value dilemma,and realize the multi-dimensional nonlinear optimization of the main grid and the auxiliary grid.The system proposed in this study takes power distribution network as the goal,applies fuzzy probability algorithm,simplifies the calculation process,avoids local extreme value,and finally realizes the energy balance between each power grid.Simulation results show that the Multi-Agent Technology enjoys priority in restoring important load,shortening the recovery time of power grid balance,and reducing the overall line loss rate of power grid.Therefore,the power grid fault self-healing system can improve the safety and stability of the important power grid,and reduce the economic loss rate of the whole power grid.

Distributed Air &Missile Defense with Spatial Grasp Technology

A high-level technology is revealed that can effectively convert any distributed system into a globally programmable machine capable of operating without central resources and self-recovering from indiscriminate damages. Integral mission scenarios in Distributed Scenario Language (DSL) can be injected from any point, runtime covering & grasping the whole system or its parts, setting operational infrastructures, and orienting local and global behavior in the way needed. Many operational scenarios can be simultaneously injected into this spatial machine from different points, cooperating or competing over the shared distributed knowledge as overlapping fields of solutions. Distributed DSL interpreter organization and benefits of using this technology for integrated air and missile defense are discussed along with programming examples in this and other fields.

An Advanced Probabilistic Neural Network for the Design of Breakwater Armor Blocks

In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.

基于专利的领域技术知识流动网络特征分析——以LTE-Advanced技术为例

运用社会网络分析方法,通过数据收集、网络构建和网络分析三个步骤实施研究。首先,从德温特专利数据库中下载所需的专利文献;然后,运用社会网络分析软件构建了LTE-Advanced技术领域的专利权人合作网络和专利权人引用网络;最后,从整体特征和关键节点两个方面对所构建的网络进行分析,力图从隐性知识和显性知识相结合的角度揭示该领域的技术知识流动网络的特征,以期对从事该领域的技术人员和企业决策有所助益,并将分析流程与方法用于其他技术领域的研究中。

LTE-Advanced网络Uu接口多协议关联方案的研究与实现

针对LTE-Advanced网络移动数据业务爆炸性增长、多协议关联分析处理效率低下等问题,在传统信令监测系统的基础上,提出了一种采用大数据技术(存储、处理和分析)和多协议关联分析技术的LTE-Advanced网络Uu接口多协议关联分析系统。首先,对Uu接口的L1、L2、L3数据业务流程进行高效处理和精准分析,然后进一步关联分析用户数据信令流程和用户业务数据流程。最后,将所提系统应用于LTE-Advanced网络Uu接口数据的现网测试中。测试结果表明:设计的多协议关联分析系统达到了预期的效果,对提升用户体验、高效进行大数据分析以及精准营销领域具有推广意义。

LTE-Advanced系统Type 1中继资源分配的研究

LTE-Advanced系统中引入了中继技术,主要用来扩大小区覆盖。而现有的传统的资源调度方案并没有考虑引入中继后回程链路和接入链路的关联性,导致两条链路速率不匹配,降低系统资源的利用率。本文提出一种基于中继缓存队列的调度机制,即根据中继的缓存队列长度动态改变用户在回程链路调度的优先级,使用户在两条链路的传输速率尽量匹配。仿真结果表明,新提出的资源调度方案可以使接入中继的用户吞吐量增加,提高系统的资源利用率。

Situation Awareness of Active Distribution Network:Roadmap,Technologies,and Bottlenecks

With the rapid development of local generation and demand response,the active distribution network(ADN),which aggregates and manages miscellaneous distributed resources,has moved from theory to practice.Secure and optimal operations now require an advanced situation awareness(SA)system so that operators are aware of operation states and potential risks.Current solutions in distribution supervisory control and data acquisition(DSCADA)as well as the distribution automation system(DAS)generally are not able to meet the technology requirements of SA.In this paper,the authors’participation in the project of developing an SA system as the basic component of a practical active distribution management system(ADMS)deployed in Beijing,China,is presented.This paper reviews the ADN’s development roadmap by illustrating the changes that are made in elements,topology,structure,and control scheme.Taking into consideration these hardware changes,a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN.The SA system’s implementation bottlenecks are also presented,including,but not limited to issues in big data platform,distribution forecasting,and security evaluation.Potential technology solutions are also provided.

Challenges for beyond 5G:ultra-densification of radio access network

Ultra-densification of radio access network(RAN)is a key to efficiently support the exponentially growing mobile data traffic in 5 G era.Furthermore,extremely high frequency band like mm Wave band was utilized to solve the bandwidth shortage problem.However,untra-dense reusing the same radio resource produced severe interference.And the mm Wave link was very harsh due to frequent blockage by obstacles.Therefore a new RAN architecture needed to be introduced to realize ultra-reliable communications in such a severe radio propagation environment.An architecture of distributed MIMO based RAN was presented.Then,enhanced interference coordination(e IC)was described.Finally,the effectiveness of distributed MIMO based RAN with e IC by computer simulation was showed.

Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these security issues,the safety protection technology of the in-vehicle system has become a focus of research.Using the advanced autoencoder network and recurrent neural network in deep learning,we investigated the intrusion detection system based on the in-vehicle system.We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior.In order to verify the accuracy and efficiency of the proposed model,it was evaluated using real vehicle data.The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior.The parameters of the model are self-iteratively updated using the time-based back propagation algorithm.We verified that the model proposed in this study can reach a nearly 96%accurate detection rate.

Exploring Content Popularity in Information-Centric Networks

Information-Centric Networking(ICN), an alternative architecture to the current Internet infrastructure, focuses on the distribution and retrieval of content by employing caches in a network to reduce network traffic. The employment of caches may be accomplished using graph-based and content-based criteria such as the position of a node in a network and content popularity. The contribution of this paper lies on the characterization of content popularity for on-path in-network caching. To this end, four dynamic approaches for identifying content popularity are evaluated via simulations. Content popularity may be determined per chunk or per object, calculated by the number of requests for a content against the sum of requests or the maximum number of requests. Based on the results, chunk-based approaches provide 23% more accurate content popularity calculations than object-based approaches. In addition, approaches that are based on the comparison of a content against the maximum number of requests have been shown to be more accurate than the alternatives.

Providing Global Awareness in Distributed Dynamic Systems

The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,organization,and functionalities.After the review of meanings of awareness and existing approaches for its expression and support,the paper shows application of the Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)for very practical awareness solutions in large distributed dynamic systems,with obtaining any knowledge from any point inside or outside the system.The self-evolving,self-replicating,and self-recovering scenario code in SGL can effectively supervise distributed systems under any circumstances including rapidly changing number of their elements.Examples are provided in SGL for distributed networked systems showing how in any node any information about other nodes and links,including the whole system,can be obtained by using network requesting patterns based on recursive scenarios combining forward and backward network matching and coverage.The returned results may be automatically organized in networked patterns too.The presented exemplary solutions are parallel and fully distributed,without the need of using vulnerable centralized resources,also very compact.This can be explained by fundamentally different philosophy and ideology of SGT which is not based on traditional partitioned systems representation and multiple agent communications.On the contrary,SGT and its basic language supervise and control distributed systems by holistic self-spreading recursive code in wavelike,virus-like,and even“soul-like”mode.

Spatial Management of Distributed Social Systems

The paper describes the use of invented,developed,and tested in different countries of the high-level spatial grasp model and technology capable of solving important problems in large social systems,which may be represented as dynamic,self-evolving and distributed social networks.The approach allows us to find important solutions on a holistic level by spatial navigation and parallel pattern matching of social networks with active self-propagating scenarios represented in a special recursive language.This approach effectively hides inside the distributed and networked language implementation traditional system management routines,often providing hundreds of times shorter and simpler high-level solution code.The paper highlights the demands to efficient simulation of social systems,briefs the technology used,and provides some programming examples for solutions of practical problems.

浅谈配网自动化光纤通信系统施工技术

为了保证配网自动化光纤通信系统的稳定运行和信号的可靠传输,论文简要介绍了配网自动化光纤通信系统,对配网自动化光纤通信技术在施工过程中的施工要求、关键要点和注意事项进行了探究。

美国培育发展未来产业的经验研究

未来产业是大国之间角力和竞争的焦点。美国不断加强量子信息科技、人工智能、先进制造、下一代通信网络和生物技术等前沿科技和未来产业发展,不同产业间的主要共性举措主要有:强化政策引导,系统研发布局;部门各司其职,相互协调推进;注重平台建设,产学研紧密融合;加强人才培养,建设多层梯队;推动国际合作,提升产业韧性等,产生了较好效果。中国可从中吸取经验,通过统筹协调、完善生态、搭建平台、培育人才和国际合作等方面持续推动未来产业发展壮大,加快培育和发展新质生产力。

路网耦合下计及电动汽车V2G潜力的充电站选址定容研究

电动汽车具备交通运输和移动负荷的双重特性,其大规模的集中充电过程会对交通网和电网同时造成冲击。利用电动汽车的移动属性和车辆到电网(V2G)技术,能够在时空层面优化其充电需求,不仅可以抑制上述冲击,还可以为电网提供额外的储能容量,协助电网削峰填谷和促进新能源消纳。以电网投资建设的充电站为例,提出一种同时考虑交通网和电网因素的充电站选址定容策略,在满足电动汽车充电需求的前提下,优化电动汽车的充电需求以最大化可用储能容量。首先,建立动态交通网模型,结合Floyd算法与区域特性精确模拟电动汽车的行驶路径,预测电动汽车充电负荷的时空特性。其次,基于电动汽车的充电负荷预测结果,以最大化储能容量、充电负荷平均分布、车辆驻车时间最长3个指标初步确定电站选址,然后再根据全时段的统计结果确定充电站的最优选址与容量。最后,以某市主城区的部分实际道路为例,预测充电负荷的时空分布,以最大储能容量为目标完成充电站的选址定容,并分析了优化结果对交通网和电网的影响,结果证明了所提方法的有效性。

基于储能技术的复杂有源配电网供电可靠性分析

分布式发电设备经过有效布局和调度可降低资源浪费,但传统方法存在布局不合理、调度效果差等问题。为此,提出基于储能技术的复杂有源配电网供电可靠性分析。首先分析分布式储能技术的基本原理,以电荷状态评价储能系统的电流剩余电量;在负荷点指标加权运算下确定电力系统中各项指标值,获取有源配电网运行过程中负荷的具体变化,简化馈线区可靠性参数,采用蒙特卡罗法抽样各层样本,运算出系统可靠性指标的期望值,确定有源配电网故障分区,根据孤岛随机变量获得系统概率可靠性指标,实现对有源配电网可靠性的全面评估和分析。实验结果表明:所提方法能够模拟电网实际运行状态,评估准确性较高。

基于BP神经网络的隧道超前地质预报资料综合解译技术研究

为提高超前地质预报资料综合解译的准确性,本次研究通过大量预报工作实践与持续开挖验证,建立了一套基于隧道掌子面地质调查法、地震波反射法和电磁波反射法于一体的隧道超前地质预报资料的综合解译规则。其中,一级综合解译基于掌子面地质编录一项指标和超前地质预报探测的四项指标分析得到,包含70条一级综合解译结果;二级综合解译在一级综合解译的基础上增加了地质雷达探测资料的两项指标,包含117条二级综合解译结果;二级综合解译比一级综合解译更详细、具体,降低了多解性出现的概率。同时,选取了70组训练样本,采用Back Propagation神经网络分析方法训练解译规则,形成了一套隧道超前地质预报综合解译技术。最后,以大巴山脉某公路隧道预报为应用实例,提出方法的预报准确率在80%以上,证明了该综合解译技术的有效性,研究可为隧道综合超前地质预报资料解译提供参考。

复杂任务情境中技术如何促进部门协同——基于S市市域社会治理现代化指挥中心的案例研究

已有研究多将技术促进部门协同理解为技术与组织互动视角下,基层政府为应对复杂任务情境采取的一种策略性行为,但对其背后的权力维度关注甚少。论文围绕权力视角下技术如何实现政府部门协同这一核心问题,选取S市市域社会治理现代化指挥中心为案例,探讨技术是否影响,以及如何影响政府部门间的权力关系。研究认为,从案例中析出的分布式治理遵从了“任务情境-技术应用-治理行为”相互适配的行动逻辑,对标复杂性任务情境,通过平台技术的协调功能,能够推动政府部门围绕“高效处置一件事”组建任务共同体。这种任务共同体暗含了以“科层为体、技术为用”的嵌套性组织结构和松散耦合性部门关系互为表里的新型权力结构——分布式权力网络。分布式治理作为根植于中国城市基层治理场景的微观理论,一方面有助于丰富基层治理机制的理论图谱,对传统基层治理机制选择的理论预设以及技术治理与科层治理共生理论进行了部分修正和改进;另一方面,通过反思技术与科层组织关系的传统研究路径,将权力维度重新拉入技术与组织关系研究,在一定程度上扩展了既有研究视域。同时,分布式治理的提出也为回答“优化基层治理数字化转型和技术化创新”之问提供了一个探索性答案。

基于人工智能技术的配网用户侧负荷态势感知方法

为解决现有配网用户侧负荷态势感知方法感知误差大的问题,提出了基于人工智能技术的配网用户侧负荷态势感知方法。分析了配网用户侧负荷异常情况,筛选出零电量用户,构建人工智能技术负荷态势感知模型,对负荷群态势进行量化分析。使用机器学习方法划分负荷空域集合,计算每一个集合的异常得分,使用雷达图法评估零电量电力用户异常程度。以态势负荷时间序列能量值为基准进行归一化处理,结合Fisher判别法对待感知态势进行动态预测,获取态势感知类别。实验结果表明,该方法与实际最高感知负荷相差0.05 MW,最高感知误差为0.20,说明使用该技术能够得到精准的感知结果。

Multi-objective optimization strategy for distribution network considering V2Genabled electric vehicles in building integrated energy system

Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical operation problems of distribution network.The system power loss and node voltage excursion can be effectively reduced,by taking measures of time-of-use(TOU)price mechanism bonded with the reactive compensation of energy storage devices.Firstly,the coordinate charging/discharging load model for EV has been established,to obtain a narrowed gap between load peak and valley.Next,a multi-objective optimization model of the distribution grid is also defined,and the active power loss and node voltage fluctuation are chosen to be the objective function.For improving the efficiency of optimization process,an advanced genetic algorithm associated with elite preservation policy is used.Finally,reactive compensation capacity supplied by capacitor banks is dynamically determined according to the varying building loads.The proposed strategy is demonstrated on the IEEE 33-node test case,and the simulation results show that the power supply pressure can be obviously relieved by introducing the coordinated charging/discharging behavior of EV;in the meantime,via reasonable planning of the compensation capacitor,the remarkably lower active power loss and voltage excursion can be realized,ensuring the safe and economical operation of the distribution system.