双馈风力发电机非线性模型预测控制

在现代风力发电厂中, 需对双馈式风力发电机(Doubly fed induction generator, DFIG)进行有效控制来确保高效率和高负荷跟踪能力. 风力发电厂包含很多不确定因素和非线性因素, 传统的线性控制器往往难以奏效. 本文针对双馈式风力发电机的功率控制提出了一种非线性模型预测控制方法. 文中的目标函数考虑了现实约束下的经济因素和设定值跟踪能力, 同时降低机组磨损和机械疲劳. 预测值可基于输入输出反馈线性化(Input-output feedback linearization, IOFL)策略来计算. 仿真实验结果验证了所构造的非线性模型预测控制器的有效性.     

基于串联结构的分布式模型预测控制

分布式模型预测控制(Distributed model predictive control, DMPC)是一类用于多输入多输出的大规模系统的控制方式.每个智能体通过相互协作完成整个系统的控制. 已有的分布式预测控制算法可以划分为迭代式算法和非迭代算法:迭代算法在迭代到收敛情况下,具有集中式预测控制(Centralized model predictive control, CMPC)算法的性能,但迭 代次数过多,子系统间通信量大;非迭代算法不需要迭代,但性能有一定损失.本文提出了一种基于串联结构的非迭代分布式预测控 制算法.本文算法在串联结构系统中可以有效减少计算量,并结合氧化铝碳分解(Alumina continuous carbonation decomposition process, ACCDP)这一串联过程,通过仿真验证了算 法的有效性;同时分析了算法运用在串联结构下的性能并证明了其稳定性.     

Direct Power Control of Dfig by Using Nonlinear Model Predictive Controller

This paper proposes a nonlinear model predictive direct power control (PDPC) strategy for a double fed induction generator (DFIG)‐based wind energy generation system. Active and reactive power variations of DFIG are calculated based on machine rules, and a nonlinear model of DFIG is given. A nonlinear model predictive controller (NMPC) is presented based on the useful cost function and constraint that it results in more proximity between simulations and reality. The power and current ripples are reduced and the optimal rotor voltage is generated based on an objective function and the constraints. The rotor voltage vector is calculated in the synchronous reference frame and transferred into the rotor reference frame. Simulation results of a 2 MW DFIG system show good performance of the proposed method during variation of active and reactive powers, machine parameters, and wind speed. Also, the transient responses of active and reactive powers are within a few milliseconds.     

网络信息模式下分布式系统协调预测控制

工业系统中广泛存在一类由多个相互关联的子系统组成的大系统. 尽管分布式控制结构的性能没有集中式控制好,但由于其具有较高的灵活性和容错性,相对于集中控制更加适合控制上述系统.在保持容错性的情况下如何提高系统的整体性能是分布式控制的一个难点问题.本文提出了一种分布式预测控制(Distributed model predictive control, DMPC)方法,该方法通过在各子系统预测控制器的性能指标中加入输入变量对其下游子系统的影响的二次函数,来扩大分布式预测控制的协调度,进而在不增加网络连通度,不改变系统容错性的前提下,提高系统的性能.另外,本文给出了基于该协调策略的带输入约束的分布式预测控制器的设计方法,在初始可行的前提下,该方法相继可行并可保证系统渐近稳定.     

Distributed Model Predictive Control with Actuator Saturation for Markovian Jump Linear System

This paper is concerned with the distributed model predictive control (MPC) problem for a class of discrete-time Markovian jump linear systems (MJLSs) subject to actuator saturation and polytopic uncertainty in system matrices. The global system is decomposed into several subsystems which coordinate with each other. A set of distributed controllers is designed by solving a min-max optimization problem in terms of the solutions of linear matrix inequalities (LMIs). An iterative algorithm is developed to achieve the online computation. Finally, a simulation example is employed to show the effectiveness of the proposed algorithm.      

风光互补发电系统的分布式模型预测控制

风光互补发电系统中,风力和光伏独立发电且两者在地理上相隔较远,彼此没有通讯交流.对此问题,提出用分布式模型预测控制的方法去解决.首先,在风光互补发电系统中存在大量的非线性环节,运用神经网络线性逼近,训练得出各个子系统的神经网络线性化模型.然后,在此基础上,基于风力优先发电、光伏配合、蓄电池必要时输出的原则,设计出满足相...     

Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation

For large-scale networked plant-wide systems composed by physically (or geographically) divided subsystems, only limited information is available for local controllers on account of region and communication restrictions. Concerning the optimal control problem of such subsystems, a neighbor-based distributed model predictive control (NDMPC) strategy is presented to improve the global system performance. In this scheme, the performance index of local subsystems and that of its neighbors are minimized together in the determination of the optimal control input, which makes the local control decision also beneficial to its neighboring subsystems and further contributes to improving the convergence and control performance of overall system. The stability of the closed-loop system is proved. Moreover, the parameter designing method for distributed synthesis is provided. Finally, the simulation results illustrate the main characteristics and effectiveness of the proposed control scheme.      

基于负载角限制的永磁同步电机模型预测控制

针对永磁同步电机电磁转矩与负载角之间强耦合及非线性的关系,并就传统直接转矩控制容易失步的问题,该文提出了一种负载角限制的策略。基于直接转矩控制基本原理对电机失步原因进行研究,重点分析负载角与转矩之间的关系,提出了对负载角限制避免电机失步的控制思想。详细介绍了基于永磁同步电机的模型预测数学模型,考虑到消除系统预测误差加入了控制延时补偿。最后通过评价函数有效地限制了电机转矩和磁链脉动,直接限制负载角确保了电机的稳态运行。通过仿真分析可看出该文提出的方案有效可行,在保持快速响应的情况下,降低了转矩和电流脉动,避免了电机失步的风险。     

Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

In this paper, distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed, which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement. Based on the feedback of the bus voltage, the deviation of the current is dispatched to each DG according to cost over the prediction horizon. Moreover, to avoid the excessive fluctuati...     

Distributed model predictive control for multiagent systems with improved consistency

This paper addresses an improved distributed model predictive control (DMPC) scheme for multiagent systems with an attempt to improving its consistency. The deviation between what an agent is actually doing and what its neighbors believe that agent is doing is penalized in the cost function of each agent. At each sampling instant the compatibility constraint of each agent is set tighter than the previous sampling instant. Like the traditional approach, the performance cost is utilized as the Lyapunov function to prove closed-looped stability. The closed-loop stability is guaranteed if the weight matrix for deviation in the cost function are sufficiently large. The proposed distributed control scheme is formulated as quadratic programming with quadratic constraints. A numerical example is given to illustrate the effectiveness of the proposed scheme.     

Feedback Linearization Based Distributed Model Predictive Control for Secondary Control of Islanded Microgrid

In this paper, a distributed Model Predictive Control (DMPC) is proposed for the secondary voltage and frequency control of islanded microgrid, where each distributed generator (DG) is controlled by a Model Predictive Control (MPC) in the secondary control layer, individually. With considering the nonlinear dynamics of DG with primary control, input‐output feedback schemes are developed for voltage and frequency control separately. Then, all MPCs use the local and neighboring nodes information to solve the optimization problem instead of communicating with a central controller. In this way, the control of the whole system is fully distributed, which allows for a plug‐and‐play. The convergence and stability analysis of the overall closed‐loop system are provided. The simulation result shows the effectiveness of the proposed method.     

多层次多模型预测控制算法的模型切换方法研究

针对多层次多模型(Multi-hierarchical multi-model, MHM)预测控制系统的模型切换问题, 在分析各通道非线性程度对模型层次切换以及层次 间模型切换过程对系统动态特性的影响的基础上, 提出了一种新的模型切换方法. 并将此方法应用到多输入多输出pH 中和过程, 仿真结果表明, 该方法有效地改善了系统工况大范围跳变时的动态性能.     

多速率分布式预测控制及其在热连轧活套系统中的应用

多智能体协调控制系统更适合采用分布式控制方式,但是处理智能体之间的耦合影响是分布式控制的一个难点.本文针对串联结构下的多智能体系统,提出一类多速率分布式预测控制策略,异步更新多智能体的控制律,能够充分考虑智能体之间的耦合影响,提高系统的稳定性,并给出了系统稳定的充分条件.最后,将多速率分布式控制算法应用到热连轧活套系统,仿真验证了方法的有效性和可行性.     

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control (T-SMC) method for load frequency control (LFC) in renewable power systems with generation rate constraints (GRC). A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks (RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.      

基于分布式模型的动车组预测控制方法

针对动车组由若干动车/拖车组成的动力单元固定编组耦 合构成,难以用集中式模型进行有效描述的问题,提出一种动车组运 行过程的分布式描述与建模方法. 基于动车组牵引/制动特性曲线和实际运 行数据,采用子空间模型辨识方法建立了动车组各动力单元的分布式状态空间模型;提出基于分布式模型的动车组预测控制方法,给出了各动力单元牵引/制动力和运 行速度同步跟踪控制算法.基于CRH380AL型动车组运行过程数据的对比仿真结果验证了本文方法的有效性.     

时延网络控制系统的模型预测控制

针对有输入约束的不确定时延网络控制系统,提出鲁棒模型预测控制方法;其中,将不确定时延建模为范数有界的输入矩阵的不确定性．给出了鲁棒性能指标的上界和系统渐近稳定的充分条件,通过在线求解LMI凸优化问题得到状态反馈控制律．仿真例子验证了该方法的有效性．     

非线性四容水箱预测控制研究

根据工业现场实际控制中实现预测控制的困难,提出了利用OPC通讯技术把Matlab与PLC/HMI工业控制系统进行集成,并实现预测控制的方案。文中还针对非线性四容水箱系统建立了机理模型,并通过在线辨识获得四容水箱模型的未知参数,从而对水箱液位进行控制。实际控制结果表明,预测控制能够很好地使水箱液位跟踪设定值,验证了该控制策略的可行性及有效性。     

双馈风电机组的自适应神经网络保性能虚拟同步机控制

近年来，可再生能源的电网渗透率逐年提升，电网对可再生能源参与一次、二次调频的需求也愈加紧迫，虚拟同步机技术（VSG）应运而生.VSG能够赋予新能源机组主动参与电网调频的能力，然而当VSG应用于双馈感应风力发电机（DFIG）时，存在动态过程中转子电流超出转子侧变流器（RSC）容量的风险.本文提出一种应用于DFIG的保性能虚拟同步控制器，通过使用误差映射函数将输出受限的系统转化为等价的不受限系统，并使用李亚普诺夫方法设计保性能控制器，保证转子电流在调频、故障穿越等强动态过程中不超过任意人为设定的限制；此外，利用神经网络自适应策略，对发电机组中的不确定动态特性进行补偿，从而获得理想的控制效果.最后，本文通过大量仿真验证了所提控制策略在调频能力、转子电流控制和应对参数偏差等方面的控制性能.     

Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems

This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC) in emergency voltage stabilization of power systems.Despite success in various applications,real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems,and in power systems,the computation time exceeds the available decision time used in practice by a large extent.This long-stan...     

Resilience Against Replay Attacks: A Distributed Model Predictive Control Scheme for Networked Multi-Agent Systems

In this paper,a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed.The methodological starting point relies on a smart use of predictive arguments with a twofold aim:1)Promptly detect malicious agent behaviors affecting normal system operations;2)Apply specific control actions,based on predictive ideas,for mitigating as much as possible undesirable domino effects resulting from adversary operations.Specifically,the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent.Finally,numerical simulations are carried out to show benefits and effectiveness of the proposed approach.