Sampled-data bipartite consensus control of nonlinear multi-agent systems under denial-of-service attacks

This article concentrates on the sampled-data secure bipartite consensus problem for a class of nonlinear multiagent systems under intermittent denial-of-service attacks. The network communication channels are destroyed when the attacks occur, which causes all the system states to be unavailable. A novel distributed sampled-data output feedback control protocol is developed by using the discontinuous sensor data. It is proved by the Lyapunov stability analysis that the considered nonlinear multiagent systems can achieve bipartite consensus exponentially in light of the designed control protocol. Then, the control algorithm is extended to systems with both state and input delay. Finally, a simulation example is presented to verify the effectiveness of the proposed control protocol.     

基于卡尔曼滤波迭代学习的交通信号控制方法

由于城市交通流具有高度的复杂性，路网内存在的非重复性干扰会使迭代学习的交通控制系统动态性能变差。因此，提出了一种卡尔曼滤波器和迭代学习的交通信号复合控制方法，以进一步改善控制系统的鲁棒性和抗干扰能力。该控制方法首先利用卡尔曼滤波器对系统的状态进行观测，在含有随机噪声干扰的情况下，估计系统的最优状态；其次设计了带遗忘因子的迭代学习控制方法，遗忘因子可增强对大幅扰动的抗干扰能力，再通过迭代学习逐渐跟踪系统的参考轨迹；最后，对该算法的收敛性进行了数学证明，而仿真的实验结果也表明在扰动环境下利用提出的方法可以降低干扰对控制系统的影响，在一定程度上提高了道路通行能力、减少了交通拥堵。     

Capability-Enhanced Microscopic Simulation With Real-Time Traffic Signal Control

An application programming interface (API) is a feature that is available in some traffic simulation programs to enhance their capabilities by allowing users to customize changes in simulation such as driver behaviors, vehicle characteristics, user-defined control strategies, and advanced Intelligent Transportation Systems (ITS) applications. This paper presents an API in AIMSUN, which is a stochastic and microscopic simulation model, to evaluate a novel real-time signal control technique based on the dynamic programming (DP) algorithm. A transportation network of diamond interchanges is first created and calibrated in the AIMSUN environment. The API, which creates a dynamic link between the DP algorithm and AIMSUN, is then developed and deployed in C $++$. During simulation runtime, real-time traffic measurements, including vehicle counts and speeds, are provided by detectors in the network and fed into the DP algorithm that subsequently makes a decision on a signal control plan. The signal plan is then transferred back to and implemented in the simulated network, which emulates its actual operation. Extensive simulations have shown that the new signal control technique is superior to other common offline signal optimization tools in terms of handling the demand fluctuations. This paper has demonstrated that the API function is a useful tool to assess new ITS applications that are unavailable in simulation programs.      

Hybrid online learning control in networked multiagent systems: A survey

This survey paper studies deterministic control systems that integrate three of the most active research areas during the last years: (1) online learning control systems, (2) distributed control of networked multiagent systems, and (3) hybrid dynamical systems (HDSs). The interest for these types of systems has been motivated mainly by two reasons: First, the development of cheap massive computational power and advanced communication technologies, which allows to carry out large computations in complex networked systems, and second, the recent development of a comprehensive theory for HDSs that allows to integrate continuous‐time dynamical systems and discrete‐time dynamical systems in a unified manner, thus providing a unifying modeling language for complex learning‐based control systems. In this paper, we aim to give a comprehensive survey of the current state of the art in the area of online learning control in multiagent systems, presenting an overview of the different types of problems that can be addressed, as well as the most representative control architectures found in the literature. These control architectures are modeled as HDSs, which include as special subsets continuous‐time dynamical systems and discrete‐time dynamical systems. We highlight the different advantages and limitations of the existing results as well as some interesting potential future directions and open problems.     

基于再励学习和遗传算法的交通信号自组织控制

提出一种将再励学习与遗传算法相结合的遗传再励学习方法对交通信号进行自组织控制,再励学习是针对第五个道路交叉口交通流的优化,修线个信号灯周期的绿性比,而遗传算法产生局部学习过程的全局优化标准,即是修正信号灯周期的大小,这种方法克服了现有的控制方法需要大量数据传输通讯、准确的交通模型等缺陷,将局部优化和全局优化统一起来。通过计算机仿真实验表明了方法有效笥。     

A multi‐agent approach to unit commitment

This paper proposes a multiagent system to power system unit commitment problems. Multiagent is a new paradigm for developing software applications. Coordinating the behavior of autonomous agents is a key issue in agent‐oriented programming techniques today. Recently, agents are being used in an increasingly wide variety of applications, ranging from comparatively small systems such as E‐mail filters to large, open, complex systems such as air traffic control. Though some agent frameworks have been proposed in the power system field, the number of studies is limited. In this paper, we developed a power system unit commitment application by multiagent architecture. Our multiagent system has the following characteristics: (1) The system consists of a single facilitator agent, two mobile agents, and one or more generator agents which are elements of power system network. (2) The facilitator agent is developed to act as a manager for the process by using the singleton design pattern. The mobile agents migrate to generator‐agents to increase or decrease their power generations. The generator agents have their operational data. (3) Message object is developed to communicate between the agents using KQML‐like object. The proposed approach is applied to a simple model system, and the results show that the multiagent system is an efficient decentralized approach for solving power system unit commitment problems. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 141(2): 41–47, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10057     

基于Forney式因子图的主动配电网状态估计

实时准确的运行数据是实现主动配电网在线运行分析与智能化控制管理的基础。为了解决配电网实时量测不足带来的估计结果不理想的问题,依据通信领域的置信传播(BP)算法,提出一种基于Forney式因子图的主动配电网状态估计方法。考虑到具体用户量测的稀缺性及分布式电源运行时受气候影响的随机性,该方法首先通过历史负荷曲线获得先验分布,为配电网建立了统计学的计及光照辐射度及风速的Forney式因子图模型,然后利用BP算法全局推理变量节点及因子节点双向传递的本地置信度和状态信息,来获得各状态变量的边缘分布。通过对某地区11节点配电网系统和IEEE 33节点配电网系统进行仿真,表明了所述方法具有良好的实时性且在配电网实时量测不足的情况下也有较理想的估计结果。     

基于约束型深度强化学习的主动配电网 电压控制策略

随着分布式电源与随机性负荷的大量接入,配电网的电压波动问题变得愈发严重。主动配电网能通过各种电压无功控制器平抑电压波动,但通常需要求解一个复杂的混合整数二阶锥规划问题,难以做到实时控制。文中利用深度强化学习建立了一个主动配电网实时电压控制模型,能快速得到满足潮流约束的控制策略。采集节点有功、节点无功、设备档位、时间步作为环境状态变量;以和网损及设备操作相关的费用作为回报函数来协调三个控制设备;通过基于长短时记忆网络的约束型强化学习来求解,从而建立主动配电网实时电压控制模型。基于4节点测试系统和IEEE-33节点测试系统进行了仿真,仿真结果表明,所提的深度强化学习方法能确保潮流约束,电压控制模型能实时控制电压无功控制器,以保证配电网的电压质量。     

A Voltage Control Method for a Distributed Line Feeder by means of Multiagent Approach

This paper proposes a method for the coordinated control of power factor by means of a multiagent approach. The proposed multiagent system consists of two types of agent: single feeder agent (F_AG) and bus agent (B_AG). In the proposed system, an F_AG plays as an important role, which decides the power factors of all distributed generators by executing the load flow calculations repeatedly. The voltage control strategies are implemented as the class definition of Java into the system. In order to verify the performance of the proposed method, it has been applied to a typical distribution model system. The simulation results show that the system is able to control very violent fluctuation of the demands and the photovoltaic (PV) generations.     

分布式电池管理系统CAN总线通信仿真试验研究

电池管理系统在新能源纯电动汽车中扮演着非常重要的角色，分布式动力电池管理系统是一种多模块控制的集成系统，各个控制模块之间CAN总线通信存在复杂性和迟滞性，针对这一问题，采用CANoe/MATLAB联合仿真的方法对其进行试验。其中，在MATLAB/Simulink仿真建模环境下建立了信号采集、SOC估算和故障诊断的多模块通信模型，同时在CANoe总线仿真平台建立了分布式电池管理系统的CAN网络架构，验证了多模块之间通信的实时性和准确性。结果表明，CANoe/MATLAB联合仿真的方法对分布式电池管理系统的通信策略开发提供了便捷。     

基于虚拟线路补偿的主动配电网混合仿真接口实现方法

数字物理混合仿真是未来主动配电网仿真分析的有效手段,而接口算法是确保其系统稳定性和精确性的关键。针对现有接口算法无法适用于主动配电网混合仿真的问题,文中提出一种基于虚拟线路补偿的改进功率接口算法。首先,在分析理想变压器法结构与稳定条件的基础上,提出在数字侧与物理侧间增设一条虚拟线路。然后,基于线路上虚拟电流对系统进行稳定性补偿,并给出了线路阻抗的取值范围。同时,针对接口固有延迟及稳定性补偿引入的误差,提出根据虚拟线路上流过的虚拟功率对接口两侧的相位差进行补偿,以保证精确性。最后,通过仿真和实验将所提方法与现有算法相比较,验证了所提方法在提升主动配电网混合仿真系统稳定性及精度上的有效性和优越性。     

基于算力-能量全分布式在线共享的5G网络负荷管理策略

5G与边缘计算等信息基础设施海量部署造成运营商用电成本上升,需推动边缘网络与电网的能量互动以节能降本。现有研究重点关注边缘网络参与日前经济调度,未考虑可再生能源和网络流量双重随机性造成的网络能量供需不平衡问题。针对强随机环境下的网络负荷管理问题,提出面向虚拟化边缘网络的能量实时管理策略。首先,以网络用能成本最小化为目标,构建联合网络资源管理、储能充放电与能量共享模型。其次,针对未来网络信息未知无法直接求解的问题,提出基于随机对偶次梯度法的在线管理策略。然后,针对资源共享涉及运营商隐私问题,提出全分布式的计算资源与能量协同共享算法。最后,仿真验证表明,所提在线算法在无需先验知识的前提下有效减少了5G边缘网络的用能成本。     

计及电压预测的主动配电网分布式电源在线优化决策方法

“双碳”背景下,配电网中分布式电源的接入量逐渐增加,如何决策分布式电源的输出设定值从而使配电网经济运行成为研究的关键问题。针对分布式电源管控问题进行了深入研究,在建立时变优化模型的基础上,就现有算法未考虑当前时段分布式电源决策对未来时段分布式电源决策影响的问题,提出了一种计及电压预测的主动配电网分布式电源优化决策方法。首先根据配电网各节点负荷变动情况对负荷变动后的网络节点电压进行预测。进一步利用所预测的配电网节点电压值进行分布式电源的决策。然后利用分布式电源决策设定值的变化量预测分布式电源决策后的网络潮流状态,以此类推从而求解所有时段分布式电源的决策设定值。针对502节点配电网算例系统,利用所提在线优化决策方法对配电网最优潮流收敛性、节点电压调控收敛性以及储能电源充放电预测有效性进行了算例验证。     

电力系统数模混合实时仿真技术的现状与发展

电力系统实时数字仿真与物理模拟相结合可以达到优势互补、优化模拟的效果。模拟仿真器与实时数字仿真系统之间能量与信号的交换 ,是数模混合实时仿真接口的主要问题。利用替代定理 ,两者之间可以通过戴维南等值电路和诺顿等值电路进行相互表述。实时数字仿真系统与HVDC原型模拟换流器构成的混合实时仿真系统在天—广高压直流控制与保护系统测试中得到了成功应用。实时数字仿真系统与动模机组的硬件连接则需要开发背靠背变流器。提出了先进的集成混合实时仿真系统的架构 ,其对经济有效地扩大实时仿真系统的规模具有重要的意义     

计及电压预测的主动配电网分布式电源在线优化决策方法

“双碳”背景下,配电网中分布式电源的接入量逐渐增加,如何决策分布式电源的输出设定值从而使配电网经济运行成为研究的关键问题。针对分布式电源管控问题进行了深入研究,在建立时变优化模型的基础上,就现有算法未考虑当前时段分布式电源决策对未来时段分布式电源决策影响的问题,提出了一种计及电压预测的主动配电网分布式电源优化决策方法。首先根据配电网各节点负荷变动情况对负荷变动后的网络节点电压进行预测。进一步利用所预测的配电网节点电压值进行分布式电源的决策。然后利用分布式电源决策设定值的变化量预测分布式电源决策后的网络潮流状态,以此类推从而求解所有时段分布式电源的决策设定值。针对502节点配电网算例系统,利用所提在线优化决策方法对配电网最优潮流收敛性、节点电压调控收敛性以及储能电源充放电预测有效性进行了算例验证。     

新型区域配电网数字物理混合仿真系统研究

随着大量分布式电源及新型电力电子设备接入配电网,迫切需要开展面向新型区域配电网的仿真分析关键技术研究,为配电网的规划、运行、科学研究与试验提供先进实用的分析技术和仿真手段。介绍了电力系统物理仿真、数字仿真和数字物理混合仿真,阐述了现有RTDS、ARENE、HYPERSIM、DDRTS等几款主流商业数字物理混合仿真系统的特点及其优缺点,并对数字物理混合仿真的分类方法进行归纳。然后从平台研发、模型建立、算法研究、接口开发、故障模拟等方面对新型配电网数字物理混合仿真技术进行了全面叙述,初步探讨新型配电网数字物理混合仿真系统研究,为进一步展开相关研究开发工作奠定基础。     

Observer‐based adaptive optimal output containment control problem of linear heterogeneous Multiagent systems with relative output measurements

This paper develops a relative output‐feedback–based solution to the containment control of linear heterogeneous multiagent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output but also optimizes their transient performance. The proposed optimal solution is composed of a feedback part, depending of the followers' state, and a feed‐forward part, depending on the convex hull of the leaders' state. To comply with most real‐world applications, the feedback and feed‐forward states are assumed to be unavailable and are estimated using two distributed observers. That is, a distributed observer is designed to measure each agent's states using only its relative output measurements and the information that it receives by its neighbors. Another adaptive distributed observer is designed, which uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state. The proposed observer relaxes the restrictive requirement of having access to the complete knowledge of the leaders' dynamics by all the followers. An off‐policy reinforcement learning algorithm on an actor‐critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requiring the leaders' dynamics. Finally, the theoretical results are verified by numerical simulations.     

基于在线学习RBF神经网络的汽门开度自适应补偿控制方法

汽门控制对于提高电力系统暂态稳定具有重要作用。为了提高汽门系统的控制性能,提出了基于在线学习RBF神经网络的汽门开度自适应补偿控制方法。首先,根据逆系统方法分析了被控汽门系统的可逆性、推导了被控汽门系统输出的α阶导数和伪控制量之间的误差,并设计了用于补偿此误差的在线学习RBF神经网络。然后,基于Lyapunov稳定性理论设计了RBF神经网络的在线学习算法,证明了闭环系统跟踪误差和RBF神经网络权值估计误差的一致最终有界性。所提出的控制方法仅需被控汽门系统很少的先验知识,而无需其精确数学模型,并且用于自适应补偿控制的RBF神经网络无需离线训练过程。最后,针对典型的单机无穷大汽门控制系统进行了数值仿真。仿真结果表明,所提出的控制方法较传统的非线性最优控制方法能明显提升电力系统的暂态控制性能。     

基于数据驱动的有源配电网实时调度降损策略

农村配网通常接入有较大容量的小水电等分布式电源,在向上级电网反送电能的过程中造成了大量的线损,因此本文提出了一种实时调度策略旨在降低配电网的线损。首先,考虑分布式电源的有功无功控制以及有载调压分接头的控制,基于支路潮流模型建立了配电网调度降损模型;进一步,通过构造高维随机矩阵,从配电网运行时间序列数据中提取能够表征运行状态的特征作为输入,对配电网历史调节策略进行热编码作为输出;然后利用深度双向长短时记忆网络学习配电网特征与网络降损策略之间的函数映射关系,建立基于数据深度学习驱动的有源配电网实时调度降损模型。最后,基于实际有源配电网系统进行了仿真,仿真结果表明所提出的实时调度算法能在保证小水电上网收益的前提下,优化小水电的出力曲线,提高分布式电能的就地消纳率,从而降低了网损。     

基于无线网络的物流车联网应用层设计研究

车联网是物联网在智能交通系统领域的典型应用,对智能物流调运系统的发展具有重要意义。本文在现有的智能交通仿真平台基础上,设计并开发了基于无线网络的物流车联网演示平台,采用模块化的设计思路,对演示平台的应用层进行设计研究,该系统能够完成网络数据接收、车队实时定位与控制、交通灯的智能监控、场景实时模拟等功能,最终实现智能物流调配。实际测试结果表明,本系统稳定性好,实时性高,操作简便,能够满足物流车联网演示平台的需求。