静止无功补偿技术及其控制方式

1静止无功补偿器的基本结构和工作原理 1.1TCR TCR由一个电抗器与两组晶闸管串联而成,如图1所示。其中两组晶闸管以相反极性并联,称相控阀。每组晶闸管一般是由多个晶闸管元件串联组成,以承受高电压和大电流。     

基于DSP的静止无功发生器的控制系统研究

本文介绍了一种以数字信号处理器(DSP)为核心的静止无功发生器(SVG)的控制系统。根据无功发生器的基本原理,阐述了基于线性鲁棒H∞理论的鲁棒控制策略,并给出了系统的硬件结构。通过实验表明了所给控制系统的有效性。     

基于系统浸入和流形不变自适应方法的静止无功补偿器非线性鲁棒自适应控制方法

本文提出一种将系统浸入和流形不变(I&I)自适应控制方法与L2-增益抑制鲁棒控制方法相结合的静止无功补偿器(SVC)的非线性鲁棒自适应控制方法.所提方法首先通过参数估计误差和鲁棒控制律的设计,使得所构造的表示参数估计误差函数的流形不变且吸引,从而使参数估计误差在这一流形上收敛于零.然后,通过所设计的可调参数对参数估计误差的收敛性能进行控制,以此来保证参数估计器对不确定参数的自适应估计能力.最后,采用自适应逆推算法推导鲁棒控制律,并通过使不确定外部扰动满足从输入到输出的耗散性来保证系统对不确定扰动的鲁棒性.仿真结果表明,利用所提方法设计的SVC控制器和参数替换律在参数估计、发电机功角动态响应方面优于传统自适应逆推算法,从而提高了输电系统的稳定水平.     

发电机的非线性自适应逆推综合控制

发电机励磁和汽门系统是一个典型的多变量、非线性、强耦合、不确定复杂系统,其综合控制将会改善电力系统稳定性和动态品质,所以设计简单、有效的综合控制器既必要又困难.针对单机无穷大励磁与汽门系统,运用自适应逆推方法和系统的Lyapunov函数,获得了发电机的非线性综合控制器和参数替换律,文中给出了该控制器的具体设计步骤.由于在控制器设计中没有运用任何线性化方法,因而所得控制器充分利用了系统的非线性特性;同时考虑了发电机阻尼系数的不确定性,使得控制器对系统参数的变化具有很强的鲁棒性.数字仿真结果表明,所设计的控制器具有鲁棒性,并可有效地提高电力系统的稳定性.     

基于DSP的静态无功补偿器的设计

研究并设计了基于DSP的TCR+FC型无功补偿装置控制器,TCR型动态无功补偿装置中触发控制角的计算精度直接影响无功补偿的效果.设计的控制器以TMS320F2182DSP芯片为核心,加上检测电路(包括采样电路、调理电路、锁相环等)和其它外围控制和驱动电路构成.设计一套低压TCR+FC系统,设计完整的软、硬件系统.     

基于SVG的矿用3.3kV静止无功动态补偿装置的研究

针对工作面供电系统电压波动大、功率因数低、线路损耗大、设备启动困难等问题,提出以SVG技术为核心的无功补偿技术路线,并介绍了一种基于SVG的矿用3.3 kV静止动态无功动态补偿装置的设计方案。工业性测试结果表明,该补偿装置响应速度快,补偿平滑准确,显著提高了工作面设备启动和运行时的功率因数,缩短了启动时间,提高了启动和运行电压的稳定性,有效抑制了谐波,并具有显著的节能效果。     

基于一体化仿真支撑环境的静止无功补偿器建模仿真

静止无功补偿技术在电力系统中得到广泛的应用,对电力系统提高功率因数、稳定电压和平衡电网等有重要意义.首先介绍了晶闸管自关断变流器的无功补偿装置的工作原理,接着在一体化仿真支撑环境中用图形化建模技术建立无功补偿器模型,并通过实验分析模型的可用性.     

无功电流检测与静止无功发生器设计

分析了低压配电网中一种无功电流检测的基本原理,提出了一种基于80C196KC单片机实现的无锁相环无功电流快速实时检测与静止无功功率发生器的设计方案,给出了无功电流检测信号调理电路原理图,通过实验研究,结果验证了该设计方案的正确性和可行性。     

基于模糊PID的静止无功补偿器电压控制方法

应用传统方法控制静止无功补偿器电压后,不仅电压回归稳态的耗时较长,且功率损耗较高。为此,本文提出基于模糊PID的静止无功补偿器电压控制方法,根据静止无功补偿器电路特点,建立数学模型,通过对补偿器电流作傅里叶变换,确定补偿器电压状态,然后,建立模糊PID控制规则,将补偿器电压修正值输入补偿器数学模型中对电压进行修正控制。应用该方法后,静止无功补偿器电压回归稳态时间在1 s以内,单位节点功率损耗在25 MW以内,电压得到有效控制。     

发电机和静止无功补偿器鲁棒非线性协调控制

采用Hamilton函数方法研究发电机励磁和静止无功补偿器(SVC)的鲁棒协调控制问题。首先建立系统的不确定非线性微分代数系统控制数学模型;然后通过预置状态反馈完成系统的耗散Hamilton实现,并基于耗散实现设计鲁棒非线性协调控制器。仿真结果表明,该控制器能有效提高电力系统的暂态稳定性。     

Hybrid adaptive robust control of static var compensator in power systems

To improve the transient response of an electric power transmission system, a hybrid adaptive robust control method is proposed in this paper for the static var compensator by incorporating the immersion and invariance adaptive (I&I adaptive) and L₂‐gain control. In contrast to the standard I&I adaptive control algorithm, establishing a target system is not required in constructing the robust control law with the proposed method. Thus, the procedure of solving PDEs to satisfy the immersion condition can be avoided. In addition, both parametric and non‐parametric uncertainties, which commonly exist in electric power transmission systems, are considered. The parametric uncertainty induced by the damping coefficient of the system is estimated by the designed adaptive law, which is constructed by ensuring the estimation error converges to zero. The non‐parametric uncertainty is caused by external disturbances and approximation errors in modeling the uncertain structure. By assuming that the L₂‐gain of the system to the non‐parametric uncertainties satisfies a dissipation inequality, we found that the robustness of the controller can be guaranteed. It is proved that all the system states are globally bounded and converge to a new stable equilibrium. Simulation results are also presented to show the effectiveness of the proposed control method in improving the transient response of the system and the convergence speed of the system states. Copyright © 2013 John Wiley & Sons, Ltd.     

基于自适应Backstepping设计的TCSC非线性鲁棒控制器

电力系统是强非线性的动态大系统,在运行中总要受到外部干扰和内部干扰的影响,从而对其稳定运行造成严重威胁.本文针对带有TCSC单机无穷大母线系统的三阶鲁棒模型,在考虑阻尼系数未知及系统受外部扰动的情况下,将自适应backstepping方法与非线性L₂增益干扰抑制理论融合,构造出系统的存贮函数,并获得非线性自适应鲁棒控制器及参数替换律.所得控制器不仅能够保证系统状态有界,而且能够有效抑制干扰对系统输出的影响.通过对单机系统的仿真结果表明采用该方法的控制器优于传统的控制器.     

Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.     

Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

考虑齿隙伺服系统的反步自适应模糊控制

针对具有未知参数和齿隙非线性的机电伺服系统,引入一种近似死区函数建立了系统的数学模型,给出了死区函数中参数的选取方法.用两个自适应模糊逻辑系统在线逼近机电伺服系统中的未知参数和非线性环节,从而避免了对每个未知参数推导自适应律.基于反步法设计了自适应模糊控制器,可抑制未知参数和齿隙非线性对系统性能的影响.采用Lyapunov方法证明了位置跟踪误差的指数收敛性.与PID控制方法对比的仿真实验表明,本文方法能够显著减小齿轮间传递力矩的振荡,并具有很好的控制精度和鲁棒性.     

倒立摆的自适应积分反步控制策略

针对直线单级倒立摆在模型参数不确定和外部扰动情况下的稳定控制问题,提出一种自适应积分反步控制策略。采用拉格朗日方程建立倒立摆系统的运动学模型,为减少稳态误差,将误差积分项引入反步法,设计了倒立摆的控制器;对含有未知参数的系统非线性状态微分方程,设计适当的Lyapunov函数推导出系统未知参数的自适应更新律,削弱了参数不确定性的影响。将自适应积分反步控制与一般的反步法控制、模糊控制及神经网络控制的仿真结果进行了对比,并在LabVIEW开发环境下进行了实物实验。结果表明,自适应积分反步法可以较为迅速且精确地完成稳定控制,较好地克服系统参数不确定及外部扰动的影响,具有较强的鲁棒性。     

输入受限的非线性系统自适应模糊backstepping控制

针对一类输入受限的非线性系统,提出了一种自适应模糊backsteppig控制器的设计方法.在控制器的设计过程当中,采用模糊系统对不确定非线性函数在线逼近;利用双曲正切函数和Nussbaum函数对系统输入饱和函数进行处理;将动态面法与backstepping法相结合解决"计算膨胀"的问题.通过Lyapunov理论分析证明了所设计的控制器能够使闭环系统所有信号半全局一致有界(SGUUB).最后应用于高超声速飞行器的攻角跟踪控制中,仿真结果表明该方法的有效性.     

降阶的鲁棒分散自适应反推控制

针对一类含有未建模动态的关联系统,考虑了降阶的鲁棒分散自适应反推控制问题.首先通过一系列坐标变换,将原系统重新参数化,然后引入降阶观测器,得到一个误差系统.基于该系统,给出了一种降阶自适应反推控制器的设计方案.证明了自适应控制系统的所有信号全局一致有界,调节误差渐近收敛到零.控制器阶次的降低使得本文的设计方案更具应用价值.     

Adaptive backstepping sliding mode control for feedforward uncertain systems

Output tracking backstepping sliding mode control for feedforward uncertain systems is considered in this article. Feedforward systems are not usually transformable to the parametric semi-strict feedback form, and they may include unmatched uncertainties consisting of disturbances and unmodelled dynamics terms. The backstepping method presented in this article, even without uncertainties differs from that of Ríos-Bolívar and Zinober [Ríos-Bolívar, M. and Zinober, A.S.I. (1999), ‘Dynamical Adaptive Sliding Mode Control of Observable Minimum Phase Uncertain Nonlinear Systems’, in Variable Structure Systems: Variable Structure Systems, Sliding Mode and Nonlinear Control, eds., K.D. Young and Ü. Özgüner. Ozguner, London, Springer-Verlag, pp. 211–236; Ríos-Bolívar, M., and Zinober, A.S.I. (1997a), ‘Dynamical Adaptive Backstepping Control Design via Symbolic Computation’, in Proceedings of the 3rd European Control Conference, Brussels]. In this article, the backstepping is not a dynamical method as in Ríos-Bolívar and Zinober (1997a Ríos-Bolívar, M and Zinober, ASI. 1997a. Dynamical Adaptive Sliding Mode Output Tracking Control of a Class of Nonlinear Systems. International Journal of Robust and Nonlinear Control, 7: 387–405.  [Google Scholar], 1999), since at each step, the control and map input remain intact, and the differentiations of the control are not used. Therefore, the method can be introduced as static backstepping. Two different controllers are designed based upon the backstepping approach with and without sliding mode. The dynamic and static backstepping methods are applied to a gravity-flow/pipeline system to compare two methods.