基于分层多代理的配电网故障自愈恢复系统

采用“岛屿划分+模型评估与修正”调节模型,无法满足功率平衡条件,导致配电网运行崩溃。为了恢复能源供电,提出了基于多代理分层的配电网故障自愈方法研究。根据配电网故障自愈结构设置了控制中心代理、母线代理和一次设备代理的三层代理结构,构建配电网故障自愈恢复模型,确定供电恢复后馈线上供电节点总数和孤岛总数。设置线路容量、孤岛区域供电模型约束条件,对故障定位与隔离。设计故障自愈恢复流程,确定电池可用容量参数,利用本地信息优化主代理收集信息内容,分析各故障点负荷能力、供电能力、传输状态,由此完成电源恢复工作。构建配电网结构,分析单个故障时刻自愈能力和24 h自愈能力,由实验结果可知,该自愈方法最高自愈恢复率可达到98.8%,能够快速实现供电恢复。     

主动网络中基于多代理系统的流量均衡*

主动网络流量均衡控制ATEC是一种在主动网络环境中,基于多代理MA系统的网络性能控制机制。该方法将多路径路由和资源分配相结合,利用非线性规划启发式算法——多路径等性能策略,实现网络流量和带宽资源的均衡配置,从而避免或减少网络拥塞的发生,达到提高网络性能的目的。着重介绍了ATEC的多代理MA系统实现,包括基本算法、一般模型、代理设计和合作模型的设计,并给出了仿真结果。     

基于协同恢复策略的主动电网供电恢复策略研究

针对目前主动配电网恢复能力有限缺陷,提出了一种基于协调恢复策略的主动配电网供电恢复模型。基于SOP原理,在理论层面充分考虑SOP位置和容量对配电网的影响。在多个SOP协同运行基础上,建立了含约束的多SOP供电恢复多目标优化函数。实验阶段,以改进的IEEE33总线系统为例,验证所提模型有效性。结果表明,与现有方法相比,所提方法能有效提高主动配电网在故障情况下的快速恢复能力。仿真结果进一步验证了所提模型对提高主动配电网恢复能力具有一定借鉴作用。     

自动化配电网模型及故障恢复算法

传统配电网模型是以联络开关和分段开关为独立对象建立的,在大型的复杂的配电网系统中,这种模型的不足之处暴露无遗计算量庞大、效率低、并行性差。论文利用面向对象的方法,以母线及区域为主对象,以开关、连接线为辅对象,抽象出一种新的配电网模型,在此基础之上,使用Petri网描述它的状态变迁,最后给出了故障恢复算法。     

多代理分层的智能配电网自愈控制研究

介绍了配电网自愈的基本概念,对传统的框架体系进行了改进,并提出一种基于智能多代理分层的配电网自愈控制技术。根据配电网的功能进行了分层,将智能多代理技术与电网的运行状态相结合,根据不同的运行状态执行不同的控制策略,并充分利用多代理技术的自治性和协同性,为智能配电网自愈控制提供理论和方法上的依据。     

多库系统的两阶段局部代理失败恢复策略

从保证全局事务的ACID性及实际的可操作性出发，提出了一种较为可靠的两阶段局部代理的工作流程，使用局部代理作为全局事务管理系统与局部数据库的中间层，模拟出符合2PC协议的状态，从而在多数据库的事务处理中使用2PC协议，在最大限度保证异构数据库自治的前提下，从一定程度上解决了全局事务的原子性保证问题和局部站点失败恢复的问题     

基于图论的主动配电网故障定位方法

针对主动配电网故障区段定位难题,提出了基于图论理论描述主动配电网动态特性的故障区段定位新方法.该方法根据主动配电网中节点与支路的电气联通关系,运用图论理论获取有向动态邻接矩阵,实时反馈网络结构运行状态变化,提升邻接矩阵描述拓扑结构动态特性的能力;同时根据不同情况的故障特征,提出单一故障和多重故障判据,通过识别故障区间类...     

柔性直流配电网故障快速恢复方法研究

利用传统方法实现柔性直流配电网故障快速恢复,存在生成故障恢复方案不是全局最优的局限性,为此提出柔性直流配电网故障快速恢复方法研究。准确定位配电网线路故障区域,综合故障恢复约束条件建立最优故障恢复数学模型,针对不同故障类型生成全局最优故障恢复方案,在算例分析中,选择柔性直流配电网作为算例,说明设计的新方法生成的故障恢复方案更具有效性,提高了电力用户的用电可靠性,有效减少了电力用户的损失。     

一个多机容错系统MPFTS故障恢复的设计与实现

本介绍我们研制的一个多机容错系统MPFTS的故障恢复技术。首先对MPFTS的结构和工作过程进行了概要介绍。在此基础上,分析了该系统不同类型故障恢复的可能性、恢复时机的选择、恢复所要达到的目标等问题。中详细讨论了利用异常中断返回技术实现系统故障恢复所涉及的数据结构及具体运行过程,并对上电升级重构这种特殊的故障恢复情况进行了分析。     

基于矩阵算法的主动配电网交直流混合网络供电恢复策略研究

考虑到直流输电技术的发展,直流配电应用于配电网中是未来配电网的研究重点。本文为解决主动配电网交直流混合网络的供电恢复问题,提出含直流配电线路的主动配电网供电恢复方案,当交流配电线路出现故障时,考虑直流侧配电线路网损、换流器以及分布式电源影响下的供电恢复方案;当直流配电线路出现故障时,考虑转换直流侧运行方式或让失电的直流负荷转入计划孤岛运行模式,以保证直流侧重要负荷的持续供电,分别提出交流侧和直流侧约束条件,并在约束条件下选取网络损耗、线路末端电压越限节点个数以及开关操作次数作为指标构建目标函数,依据矩阵算法对供电恢复过程的不同恢复方案进行大数据分析与处理,得出最优方案。通过改进的IEEE 123节点算例证明,提出的方案能够有效的解决主动配电网的供电恢复问题。     

Strategic best-response learning in multiagent systems

We present a novel and uniform formulation of the problem of reinforcement learning against bounded memory adaptive adversaries in repeated games, and the methodologies to accomplish learning in this novel framework. First we delineate a novel strategic definition of best response that optimises rewards over multiple steps, as opposed to the notion of tactical best response in game theory. We show that the problem of learning a strategic best response reduces to that of learning an optimal policy in a Markov Decision Process (MDP). We deal with both finite and infinite horizon versions of this problem. We adapt an existing Monte Carlo based algorithm for learning optimal policies in such MDPs over finite horizon, in polynomial time. We show that this new efficient algorithm can obtain higher average rewards than a previously known efficient algorithm against some opponents in the contract game. Though this improvement comes at the cost of increased domain knowledge, simple experiments in the Prisoner's Dilemma, and coordination games show that even when no extra domain knowledge (besides that an upper bound on the opponent's memory size is known) is assumed, the error can still be small. We also experiment with a general infinite-horizon learner (using function-approximation to tackle the complexity of history space) against a greedy bounded memory opponent and show that while it can create and exploit opportunities of mutual cooperation in the Prisoner's Dilemma game, it is cautious enough to ensure minimax payoffs in the Rock–Scissors–Paper game.     

Active fault‐tolerant consensus control of Lipschitz nonlinear multiagent systems

This article investigates the active fault‐tolerant consensus problem for Lipschitz nonlinear multiagent systems under detailed balanced directed graph and actuator faults. First, a fault detection filter for each agent is designed, and all agents can be divided into two categories: healthy agents and possibly faulty agents. Second, fully distributed adaptive fault‐tolerant consensus protocols for healthy and possibly faulty agents are proposed to achieve state consensus. Third, based on the fault detection method and fault‐tolerant consensus protocols, active fault‐tolerant consensus algorithms are given. Simulation examples are presented to verify the effectiveness of the proposed active fault‐tolerant algorithms.     

Differential evolution using ancestor tree for service restoration in power distribution systems

Problems in power distribution system reconfiguration (PDSR), such as service restoration, power loss reduction, and expansion planning, are usually formulated as complex multi-objective and multi-constrained optimization problems. Several evolutionary algorithms (EAs) have been developed to deal with PDSR problems, but the majority of EAs still demand high running time when applied to large-scale distribution systems (thousands of buses and switches). This paper presents a new approach for service restoration in large scale distribution systems that employs a discrete differential evolution with ancestor tree (DE-Tree). We combine the node-depth encoding (NDE) to represent computationally the electrical topology of the system and the ancestor tree presented here to implement differential evolution for service restoration problems. The ancestor tree is used to build a list of elementary movements that maps one solution in the search space into another, thus capturing the “difference” between forests encoded with the NDE, which is essential in the search engine of differential evolution. The use of an ancestor tree is not only central to implement differential mutation in our algorithm but also can track the sequence of switching operations in the restoration of the system after the optimization process is finished. The proposed approach makes differential evolution suitable for treating combinatorial optimization problems related to PDSR. Results presented on distribution system reconfiguration problems suggest the benefits and fast convergence of the proposed approach.     

What evolutionary game theory tells us about multiagent learning

This paper discusses If multi-agent learning is the answer, what is the question? [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365-377, this issue] from the perspective of evolutionary game theory. We briefly discuss the concepts of evolutionary game theory, and examine the main conclusions from [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365-377, this issue] with respect to some of our previous work. Overall we find much to agree with, concluding, however, that the central concerns of multiagent learning are rather narrow compared with the broad variety of work identified in [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Inteligence 171 (7) (2007) 365-377, this issue].     

游戏网格服务划分算法研究*

为了充分利用游戏网格的计算资源,使用其强大的并行计算能力,部署在游戏网格的网络游戏必须要划分成可以并行的多个服务。提出了一种基于动态二叉树的游戏网格服务划分算法;讨论了如何采用二叉树的数据结构来组织服务节点并根据服务节点的负载动态调整其服务划分;最后实现一个模拟游戏网格环境,通过实验结果证明该算法可以取得良好的性能。     

基于博弈论的多跳无线网络拓扑结构控制

为了构建低耗能、高连通性、低干扰并具有合理路由的多跳无线网络,应用基于博弈论的网络拓扑结构控制解决方案,为节点设计了一种新颖的收益函数。理论分析表明,根据此收益函数,网络将收敛于一个理想的稳定状态（纳什均衡点）,并通过最佳响应算法可获得该稳定状态。仿真结果显示,应用此方案和收益函数进行拓扑控制能形成性能良好的网络。     

Output consensus for heterogeneous multiagent systems with Markovian switching network topologies

This paper investigates the output consensus problem of heterogeneous continuous‐time multiagent systems under randomly switching communication topologies. The switching mechanism is governed by a time‐homogeneous Markov process, whose states correspond to all possible communication topologies among agents. A novel dynamic consensus controller is proposed. The controller gains are designed based on the information of the expectation graph and the solutions to regulator equations. Furthermore, a necessary and sufficient condition is presented for output consensus of the controlled multiagent system in mean square sense. Finally, a simulation example is provided to corroborate the effectiveness of the proposed controller.     

A multiagent dynamic interaction testbed: Theoretic framework, system architecture and experimentation

Recent research on Distributed Artificial Intelligence(DAI)has focused upon agents‘ interaction in Multiagent System.sThis paper presents a text understanding oriented multiagent dynamic interaction testbed(TUMIT);the theoretic framework based upon game theory,the free-market-like system marchitecture,and experimentation on TUMIT.Unlike other DAI testbeds,TUMIT views different text understanding(TU)methods as different“computational resources”,and makes agents choose different TU paths and computational resources according to the resouce information on the bulletins in their hostcomputer.Therefore,in TUMIT,task allocation is wholly distributed.This makes TUMIT work like a “free market”.In such a system,agents‘choices and resource load may oscillate.It is shown theoretically and experimentally that if agents use multi-level of “history information”,their behavior will tend to converge to a Nash equilibrium situation;and that if agents use “fecall-forget” strategy on “history information”,the convergence can be accelerated and the agents can acclimate themselves to changed environment.Compared with other DAI testbeds,TUMIT is more distributed,and the agents in TUMIT are more adaptive to the dynamic environment.     

Sliding mode coordination control for multiagent systems with underactuated agent dynamics

In this paper, we develop a new integrated coordinated control and obstacle avoidance approach for a general class of underactuated agents. We use graph-theoretic notions to characterise communication topology in the network of underactuated agents as determined by the information flow directions and captured by the graph Laplacian matrix. Obstacle avoidance is achieved by surrounding the stationary as well as moving obstacles by elliptical or other convex shapes that serve as stable periodic solutions to planar systems of ordinary differential equations and using transient trajectories of those systems to navigate the agents around the obstacles. Decentralised controllers for individual agents are designed using sliding mode control approach and are only based on data communicated from the neighbouring agents. We demonstrate the efficacy of our theoretical approach using an example of a system of wheeled mobile robots that reach and maintain a desired formation. Finally, we validate our results experimentally.     

Controllability of multiagent systems based on path and cycle graphs

This paper studies the controllability of multiagent systems based on path and cycle graphs. For paths or cycles, sufficient and necessary conditions are presented for determining the locations of leaders under which the controllability can be realized. Specifically, the controllability of a path is shown to be determined by a set generated only from its odd factors, and the controllability of a cycle is determined by whether the distance between 2 leaders belongs to a set generated from its even (odd) factors when the number of its nodes is even (odd). For both graphs, the dimension of the controllable subspace is also derived. Moreover, the technique used in the derivation of the above results is further used to get sufficient and necessary conditions for several different types of graphs generated from path and cycle topologies. These types of graphs can be regarded as typical topologies in the study of multiagent controllability, and accordingly the obtained results have meaningful enlightenment for the research in this field.