A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

Battery energy storage systems(BESSs)are widely used in smart grids.However,power consumed by inner impedance and the capacity degradation of each battery unit become particularly severe,which has resulted in an increase in operating costs.The general economic dispatch(ED)algorithm based on marginal cost(MC)consensus is usually a proportional(P)controller,which encounters the defects of slow convergence speed and low control accuracy.In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance,we attempt to design a proportional integral(PI)controller with a reset mechanism(PI+R)to asymptotically promote MC consensus and total power mismatch towards 0 in this paper.To be frank,the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing,which accelerates convergence,improves control accuracy,and avoids overshoot.The eigenvalues of the system under a PI+R controller is well analyzed,ensuring the regularity of the system and enabling the reset mechanism.To ensure supply and demand balance within the isolated BESSs,a centralized reset mechanism is introduced,so that the controller is distributed in a flow set and centralized in a jump set.To cope with Zeno behavior and input delay,a dwell time that the system resides in a flow set is given.Based on this,the system with input delays can be reduced to a time-delay free system.Considering the capacity limitation of the battery,a modified MC scheme with PI+R controller is designed.The correctness of the designed scheme is verified through relevant simulations.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

基于DCS-惯性权重组合的柔性机械臂运动轨迹控制系统研究

为了有效调控机械臂运动幅度,避免柔性机械臂实际运动轨迹与目标轨迹发生较大的偏差,设计一种基于分散控制系统(DCS)-惯性权重组合的柔性机械臂运动轨迹控制系统,它是一个集合了计算机、通信、显示和控制的计算机系统,该系统由过程控制和过程监控。通过DCS算法控制监测对象行为幅度并计算柔性机械臂惯性权重,利用DCS-惯性权重组合求解素数控制指标的具体数值,实现了对柔性机械臂运动轨迹控制系统的软件设计。实验结果表明,DCS-惯性权重组合算法作用下的机械臂末端坐标在横轴、纵轴、空间轴方向上的运动幅值均可以被控制在10个单位长度之内,并且在0.52 s内进入机械臂运动轨迹最佳控制状态,运动轨迹差值最大仅为0.01 rad,验证了该系统具备可行性和有效性。

基于CNN的OCR技术在核电厂DCS系统测试中的应用和实现

核电厂分布式控制系统(DCS)的输入/输出模块在运行期间,随着电子器件性能下降以及时漂的影响,需要定期进行进度复测和校准。针对传统的DCS输入/输出通道测试覆盖率不足,测试效率低及人因问题,本文提出一种基于卷积神经网络(CNN)的光学字符识别(OCR)技术应用在DCS系统的在线测试方法。通过模拟设备和视频采集设备完成画面的自动切换,并读取画面信息,截取特定画面后对其图像预处理,再使用OCR识别模型识别画面内容,将得到的识别结果与期望值进行比较判断,从而实现自动化测试。测试结果表明,通过CNN训练后,显控设备画面字符识别率能达到100%,该方法可以突破设备厂家的专有通信协议的壁垒,可有效降低操作员的人因失误,提升测试效率和核电厂的经济性。

基于SCADA-DCS通讯技术的油气开采数据自动化采集与控制系统

设计基于SCADA-DCS通讯技术的油气开采数据自动化采集与控制系统。系统以SCADA服务器为上层监控服务器,通过与井站现场仪器仪表实时通信,监控整个开采过程。当油气缓冲罐液位越限时,SCADA服务器自动下达指令至原油处理站DCS,启动基于智能PID的油泵控制器,调节油泵变频器,控制油气液位不溢出。系统使用SCADA-DCS通讯技术的MODBUS通信协议,并根据通信中断优先级分配方法,确保对中断处理时间要求高的通信请求能实时响应。实验结果表明,该系统能提升油气液位控制和紧急信息通讯效率。

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph.It is of two-degree-of-freedom nature.Specifically,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances.The condition for asymptotic disturbance rejection is derived.Moreover,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection performance.Then,a robust stabilization condition with less conservativeness is derived for the whole multi-agent system.Further,a design algorithm is given.Finally,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

Data-Driven Control of Distributed Event-Triggered Network Systems

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

A Two-Layer Fuzzy Control Strategy for the Participation of Energy Storage Battery Systems in Grid Frequency Regulation

To address the frequency fluctuation problem caused by the power dynamic imbalance between the power system and the loadwhen a large number of newenergy sources are connected to the grid,a two-layer fuzzy control strategy is proposed for the participation of the energy storage battery system in FM.Firstly,considering the coordination of FM units responding to automatic power generation control commands,a comprehensive allocation strategy of two signals under automatic power generation control commands is proposed to give full play to the advantages of two FM signals while enabling better coordination of two FM units responding to FM commands;secondly,based on the grid FM demand and battery FM capability,a double-layer fuzzy control strategy is proposed for FM units responding to automatic power generation control commands in a coordinated manner under dual-signal allocation mode to precisely allocate the power output depth of FM units,which can control the fluctuation of frequency deviation within a smaller range at a faster speed while maintaining the battery charge state;finally,the proposed Finally,the proposed control strategy is simulated and verified inMatlab/Simulink.The results show that the proposed control strategy can control the frequency deviation within a smaller range in a shorter time,better stabilize the fluctuation of the battery charge level,and improve the utilization of the FM unit.

EAST Cryogenic Supervisory and Control System Based on Delta-V DCS

The control system for the Experimental Advanced Superconductive Tokamak （EAST） cryogenic system is designed and constructed based on Delta-V DCS （Distribution Control System）, which consists of engineering workstations, operator workstations, application workstations, redundant controller units, input/output （I/O） cards and a redundant control network. Our task is to design a supervisory and control system to provide the operator interface for control and monitoring, sending alarms, archiving of selected signals, and other routines to analyze realtime and historic data. The hardware configuration, software structure and control algorithms are illustrated in detail in this paper. Hvpothetic oroblems and further research are also mentioned.

DCS devices based non-linear process control system design for plants with distributed time-delay using particle filter

Remote control process system with distributed time-delay has attracted much attention in different fields.In this paper,non-linear remote control of a single tank process system with wireless network is considered.To deal with the distributed time-delay in a large-scale plant,the time-delay compensation controller based on DCS devices is designed by using operator theory and particle filter.Distributed control system(DCS)device is developed to monitor and control from the central monitoring room to each process.The particle filter is a probabilistic method to estimate unobservable information from observable information.First,remote control system and experimental equipment are introduced.Second,control system based on an operator theory is designed.Then,process system with distributed time-delay using particle filter is carried out.Finally,the actual experiment is conducted by using the proposed time-delay compensation controller.When estimating with the proposed method,the result is close to the case in which the distributed time-delay does not exist.The effectiveness of the proposed control system is confirmed by experiment results.

Modeling, Analysis and Simulation of a High-Efficiency Battery Control System

This paper explains step-by-step modeling and simulation of the full circuits of a battery control system and connected together starting from the AC input source to the battery control and storage system.The three-phase half-controlled rectifier has been designed to control and convert the AC power into DC power.In addition,two types of direct current converters have been used in this paper which are a buck and bidirectional DC/DC converters.These systems adjust the output voltage to be lower or higher than the input voltage.In the buck converters,the main switch operates in conduction or cut-off mode and is triggered by a Pulse-Width Modulated(PWM)signal.The output and input voltage levels ratio are used to calculate thePWMsignal’s duty cycle.Therefore,the duty cycle indicates the operation mode of the converter in steady-state operation.In this study,we analyze and control of a buck converter with the PWM signal.Besides,the bidirectional DC/DC converter has been achieved and optimized by PI control methods to control the battery charging and discharging modes.The simulation has been applied via the Matlab/Simulink environment.The results show the activity of each part of the designed circuits starting from the converters and the battery control system in charge and discharge modes.

Stability Analysis and Control of DC Distribution System with Electric Vehicles

The DC distribution network system equipped with a large number of power electronic equipment exhibits weak damping characteristics and is prone to low-frequency and high-frequency unstable oscillations.The current interpretation of the oscillation mechanism has not been unified.Firstly,this paper established the complete statespace model of the distribution system consisting of a large number of electric vehicles,characteristic equation of the distribution network system is derived by establishing a state-space model,and simplified reduced-order equations describing the low-frequency oscillation and the high-frequency oscillation are obtained.Secondly,based on eigenvalue analysis,the oscillation modes and the influence of the key system parameters on the oscillation mode are studied.Besides,impacts of key factors,such as distribution network connection topology and number of dynamic loads,have been discussed to suppress oscillatory instability caused by inappropriate design or dynamic interactions.Finally,using the DC distribution example system,through model calculation and time-domain simulation analysis,the correctness of the aforementioned analysis is verified.

DCS控制器中电表接触传感器计量误差检测方法

由于DCS控制器中电表传感器在计量检测过程中,传统的B-MAC-DCS协议能耗和丢包率较高,无法缓解汇聚节点的漏斗效应,导致在远程抄表过程中传感器计量误差增大。提出一种机械电表接触传感器计量误差检测方法。采用小波基函数对DCS控制器中的接触传感器计量数据抗干扰处理,并通过动态选取阈值的方法,对经过小波变换后的数据去除噪声。使用低功耗自适应集簇分层型(low energy adaptive clustering hierarchy,LEACH)协议分簇代替传统的B-MAC协议;根据簇内监测值,引入阈值分析方法获取传感器计量指标,并将其作为判定依据进行误差检测,根据计量指标的变化情况判断是否存在计量误差。实验结果表明,所提方法可以准确且有效检测出机械电表接触传感器计量误差,解决DCS中机械电表的运行隐患问题。

Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

核电厂安全级DCS缺省值设置策略研究

针对数字化仪控系统中无效信号的质量位随意蔓延使系统处于一种不确定状态的问题,结合核电厂运行工况和信号特性,对龙鳞平台故障诊断机制和信号质量位标识进行研究。考虑故障安全准则,系统性地提出缺省值设置原则。从信号执行功能和信号边界两个维度进行分析,确认缺省值的设置范围,并详细给出执行保护功能、报警功能、维护和试验功能信号的缺省值设置策略。同时,针对传统的缺省值验证方式无法全面、有效地进行缺省值验证的问题,提出一种利用全范围模拟机和虚拟数字化控制系统(DCS)进行缺省值验证的新方法。利用该方法可有效地对DCS内设置的缺省值进行系统性的验证。所提出的缺省值设置策略和验证方法可为后续核电厂安全级DCS的缺省值分析和设置提供全面的指导。

An Adaptive Control Strategy for Energy Storage Interface Converter Based on Analogous Virtual Synchronous Generator

In the DC microgrid,the lack of inertia and damping in power electronic converters results in poor stability of DC bus voltage and low inertia of the DC microgrid during fluctuations in load and photovoltaic power.To address this issue,the application of a virtual synchronous generator(VSG)in grid-connected inverters control is referenced and proposes a control strategy called the analogous virtual synchronous generator(AVSG)control strategy for the interface DC/DC converter of the battery in the microgrid.Besides,a flexible parameter adaptive control method is introduced to further enhance the inertial behavior of the AVSG control.Firstly,a theoretical analysis is conducted on the various components of the DC microgrid,the structure of analogous virtual synchronous generator,and the control structure’s main parameters related to the DC microgrid’s inertial behavior.Secondly,the voltage change rate tracking coefficient is introduced to adjust the change of the virtual capacitance and damping coefficient flexibility,which further strengthens the inertia trend of the DC microgrid.Additionally,a small-signal modeling approach is used to analyze the approximate range of the AVSG’s main parameters ensuring system stability.Finally,conduct a simulation analysis by building the model of the DC microgrid system with photovoltaic(PV)and battery energy storage(BES)in MATLAB/Simulink.Simulation results from different scenarios have verified that the AVSG control introduces fixed inertia and damping into the droop control of the battery,resulting in a certain level of inertia enhancement.Furthermore,the additional adaptive control strategy built upon the AVSG control provides better and flexible inertial support for the DC microgrid,further enhances the stability of the DC bus voltage,and has a more positive impact on the battery performance.

DCS在电除尘系统自动控制的应用研究

传统的电除尘器自动控制系统采用可编程逻辑控制器(PLC)控制,其逻辑复杂、可读性差、可维护性低。PLC系统无法与主控系统完成一体化,导致工艺上的割裂,额外地增加了运维成本。本文提出了一种基于分散控制系统(DCS)的电除尘器自动控制方案。

DCS系统在燃气输配控制中的应用研究进展

为探讨DCS系统在燃气输配控制中的应用,并分析其通过实时监测和控制关键参数来提高系统效率和安全性的能力。采用DCS系统对天然气输配网络进行实时监测和控制。通过对天然气管道的压力、温度、流量等关键参数进行实时采集和调节,确保系统运行在稳定、高效的状态。同时,利用DCS系统的强大数据采集、处理和分析能力,对输配过程中的故障和异常情况进行快速诊断和处理,以提高故障排除的效率。此外,DCS系统还支持网络化和远程控制功能,实现对不同地理位置的天然气输配网络的集中监管和管理。结果表明:通过应用DCS系统,燃气输配控制实现了高效、准确和可靠的管理。实时监测和调节关键参数确保了系统运行的安全性和稳定性。快速诊断和处理故障和异常情况提高了故障排除的效率。网络化和远程控制功能实现了对不同地理位置的天然气输配网络的统一监管和管理,提高了管理效率。DCS系统在燃气输配控制中的应用具有重要意义和潜力。通过实时监测和控制关键参数,可以提高系统的效率和安全性。然而,该系统的应用也面临一些挑战,如高成本、技术难题以及应急处理等方面的问题。因此,需要进一步的技术研发和应用实践来不断提升DCS系统的性能和可靠性,以更好地满足燃气输配控制的需求。

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.