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.     

电网电压跌落下双馈风力发电机的动态响应

随着以双馈风力发电机为主体的大型风力发电机组在电网中所占比例的提高,电力系统对并网风力发电机在电网电压骤降故障下的不间断运行能力提出了更高的要求。分析了变速恒频双馈风力发电机组的工作原理,建立了双馈风力发电机组的动态数学模型,搭建了双馈风力发电机组的仿真模型,仿真分析了不同电网故障下,变速恒频双馈风力发电机组的动态响应。利用1.5MW双馈风力发电机进行了不同电网电压跌落下的试验,试验结果验证了系统在电网电压跌落故障下的动态响应特性。     

An adaptive control scheme for doubly fed induction generators – wind turbine implementation

This paper presents a new adaptive scheme for doubly fed induction generators (DFIGs) in order to augment their flexibility confronting with unpredicted operational conditions. Due to large fluctuations in the wind velocity, the proposed scheme would handle system unreliable operational conditions. In such system, which has multi-input, multi-output and is also represented as a nonlinear control system, the uncertain parameters would affect the operational conditions. So, in order to have a robust controlling scheme, the mentioned characteristics should be considered in the proposed method. The adaptive control scheme proposed in this paper satisfies the expected constraints and could also be implemented in real-world platforms, especially in large-scale wind farms with DFIG turbines.     

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.     

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.     

双馈风机自适应神经分散协调预测控制

目前,双馈感应发电机转子侧励磁控制系统均依据\"孤立\"模型设计.\"孤立\"模型忽略了各子系统之间、各控制器之间的相互作用,因此这种控制器仅对改善本系统的控制特性有一定作用.针对以上情况,提出了一种自适应神经分散协调控制策略,并将其应用于双馈感应发电机转子侧励磁控制系统仿真研究中.首先,利用电力关联测量法建立了基于本地变量的双馈风机关联测量模型.其次,以关联测量模型作为预测模型,采用多模型预测控制器对双馈风机转子侧励磁系统进行控制.最后,利用可在线调整的人工神经网络作为多模型加权控制器以补偿双馈风机强非线性、工作区间变化范围大的特点.主导特征值分析和动态仿真表明:该控制策略不仅实现了高精度的最大功率跟踪控制,而且在电力系统故障时可提供持续的、充足的阻尼.     

Decentralized Resilient H∞ Load Frequency Control for Cyber-Physical Power Systems Under DoS Attacks

This paper designs a decentralized resilient H_∞ load frequency control (LFC) scheme for multi-area cyber-physical power systems (CPPSs). Under the network-based control framework, the sampled measurements are transmitted through the communication networks, which may be attacked by energy-limited denial-of-service (DoS) attacks with a characterization of the maximum count of continuous data losses (resilience index). Each area is controlled in a decentralized mode, and the impacts on one area from other areas via their interconnections are regarded as the additional load disturbance of this area. Then, the closed-loop LFC system of each area under DoS attacks is modeled as an aperiodic sampled-data control system with external disturbances. Under this modeling, a decentralized resilient H_∞ scheme is presented to design the state-feedback controllers with guaranteed H_∞ performance and resilience index based on a novel transmission interval-dependent loop functional method. When given the controllers, the proposed scheme can obtain a less conservative H_∞ performance and resilience index that the LFC system can tolerate. The effectiveness of the proposed LFC scheme is evaluated on a one-area CPPS and two three-area CPPSs under DoS attacks.      

矩阵变换器交流励磁系统的非线性最优控制

传统的矩阵变换器交流励磁控制策略是基于PI调节器的功率解耦控制,但在电网电压出现大扰动时,系统调节时间较长,影响发电机的安全稳定运行.本文基于非线性微分几何理论,证明了双馈电机(DFIG)动态模型满足完全线性化条件,建立了线性化的动态模型,在此模型基础上采用最优控制求出相应控制率,再结合矩阵变换器的间接空间矢量调制策略...     

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.     

电网故障下双馈风力发电机网侧变换器的高阶滑模控制

针对电网故障下对双馈风力发电机网侧变换器的影响,本文采用滑模变结构控制方法,提出了采用增加瞬时功率前馈补偿的高阶滑模变结构控制策略,为此首先简要的分析了双馈风力发电机中双脉宽调制（PWM）型变换器的结构和控制原理,然后根据网侧变换器的数学模型,提出对网侧变换器采用电网电压定向欠量控制,以实现交流侧单位功率因数和直流环节电压控制．提出的方法提高了系统响应速度,克服参数变化和外电压波动的影响．仿真结果表明了该策略的有效性．     

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.     

双馈变速恒频风力发电系统的并网控制研究及其仿真实现

随着风电机组单机容量的不断增大,发电机并网时的电流冲击已不能忽视,必须对并网控制技术进行深入研究。在总结现有风力发电并网技术的基础上,研究双馈变速恒频风力发电机的“柔性并网”问题,即可通过励磁控制调节发电机输出并满足并网条件。研究双馈电（DFIG）的空载模型、变频器的整流控制模型和并网控制模型,建立整个并网发电系统,并通过完整的仿真验证系统建模与控制的有效性。     

Stability conditions for a class of 2D continuous-discrete linear systems with dynamic boundary conditions

Repetitive processes are a distinct class of 2D systems of both practical and theoretical interest. Their essential characteristic is repeated sweeps, termed passes, through a set of dynamics defined over a finite duration with explicit interaction between the outputs, or pass profiles, produced as the process evolves. Experience has shown that these processes cannot be studied/controlled by direct application of existing theory (in all but a few very restrictive special cases). This fact, and the growing list of applications areas, has prompted an on-going research programme into the development of a 'mature' systems theory for these processes for onward translation into reliable generally applicable controller design algorithms. This paper develops stability tests for a sub-class of so-called differential linear repetitive processes in the presence of a general set of initial conditions, where it is known that the structure of these conditions is critical to their stability properties.     

基于纳什议价的无人机编队自主重构控制方法

针对任务环境下携带不同载荷的无人机(Unmanned aerial vehicles, UAVs)组成的编队, 为实现无人机间的相互支援和补充而进行编队重构控制, 运用多目标多人博弈理论, 将其转化为纳什谈判过程. 结合分布式模型预测控制(Distributed model predictive control, DMPC)方法, 设计一种基于纳什谈判的分布式预测控制(Nash bargaining solution-DMPC, NBS-DMPC)算法求解该问题, 并对算法收敛性进行了证明. 仿真实验表明, 该算法能够有效控制编队自主重构, 实现编队无人机间的威胁规避和协同保护, 同时能够有效降低无人机编队自主重构控制问题的求解规模.