基于PSIM的并联型有源电力滤波器的仿真研究

并联型有源电力滤波器能够补偿非线性负载产生的谐波电流、抑制电网电流谐波含量,提高电网的电能质量。文中提出了并联型有源电力滤波器PAPF(Parallel active power filters)新型控制方案,通过仿真验证了分析的正确性。     

基于预测函数模型的APF补偿电流控制研究

为了有效地改善电网电流中因接入非线性负载所引入的谐波分量和削弱控制系统的延时特点,提出了一种基于预测函数模型的有源电力滤波器(APF)补偿电流控制方法,由当前时刻采样数据和最近历史时刻的数据进行构建预测函数模型,实现了有源滤波器谐波补偿电流的预测控制.仿真结果表明:该控制方法不仅对负载电流有精确的预测能力,且对系统电流中谐波电流具有较好的抑制效果和补偿精度.     

三电平SAPF双闭环控制系统的研究

提出了一种三电平并联型有源电力滤波器双闭环控制系统的设计方案。该系统采用ip-iq法提取谐波,利用瞬时无功功率理论检测三相电压与负载电流,用以计算补偿电流;其电压外环采用模糊PI控制器控制,电流内环采用内模控制器控制,并由三电平SVPWM调制器输出补偿电流。仿真实验结果验证了该系统具有良好的谐波补偿效果。     

无源性控制在有源电力滤波器中的应用

线路或负载参数未知可能会导致有源滤波器系统失稳，为解决系统的稳定性问题，将无源性控制应用于有源电力滤波器来保证系统的稳定性。将有源滤波器和非线性负载视为一个整体，对负载进行诺顿等效，把负载电压和电源电流作为状态变量，把补偿电流作为控制变量，建立系统在dp轴上的模型。获得系统的控制律也就获得了补偿电流的给定值，验证系统满足无源性条件之后，再利用无源性控制方法获得系统的控制律。虽然算法在理论上比较复杂，但是实现上相当简单。在仿真中，对比了容性负载，感性负载以及变动负载情况下的控制效果，仿真结果表明算法具有很好的鲁棒性，并能很好的保证系统的稳定性。     

一种新型有源电力滤波器电流控制策略研究

针对现有有源电力滤波器结构较为复杂、谐波电流跟踪困难等问题,提出了一种新型有源电力滤波器电流控制策略。该控制策略不需要进行负载电流测量与谐波检测,而是直接在电网侧提取电网电流,并将比例积分与矢量比例积分控制器结合使用以对电网电流进行控制,从而实现谐波补偿。仿真结果证明了该策略的正确性与有效性。     

三相三线APF补偿电流的模糊反馈检测与控制

补偿电流的检测是三相有源电力滤波器的关键问题,快速准确地检测出补偿电流并实施有效的控制直接影响到有源电力滤波器的补偿效果.对三相三线制有源电力滤渡器进行电路模型分析,采用T-S模糊反馈控制的方法,获得三相三线制有源电力滤波器的补偿电流,并对电源电流进行实时的模糊反馈控制.采用并行分布补偿的方法设计了模糊反馈控制器,将有源电力滤波器的非线性问题线性化,在稳定性条件下求解线性矩阵不等式,得到无功功率及谐波电流全补偿控制策略的状态反馈增益,由此获得连续的即时补偿跟踪电流,仿真及实验结果验证了补偿电流检测的准确性及模糊反馈控制的有效性,对简化有源滤波器的电流检测与其构成组件具有较大应用价值.     

无源性控制在有源电力滤波器中的应用

线路或负载参数未知可能会导致有源滤波器系统失稳. 为解决系统的稳定性问题, 将无源性控制应用于有源电力滤波器来保证系统的稳定性. 将有源滤波器和非线性负载视为一个整体. 对负载进行诺顿等效, 把负载电压和电源电流作为状态变量, 把补偿电流作为控制变量, 建立系统在dq轴上的模型. 获得系统的控制律也就获得了补偿电流的给定值. 验证系统满足无源性条件之后, 再利用无源性控制方法获得系统的控制律. 虽然算法在理论上比较复杂, 但是实现上相当简单. 在仿真中, 对比了容性负载, 感性负载以及变动负载情况下的控制     

基于LCL型有源电力滤波器的复合电流控制策略研究

针对传统电流控制策略存在高稳态精度与快速动态响应的矛盾,提出了一种基于LCL型有源电力滤波器的复合电流控制策略。该控制策略将比例积分控制和重复控制有机结合,在保证动态响应的基础上,利用PI控制器将控制模型补偿为稳定系统,使其在低频段具有良好的控制特性;利用重复控制器校正LCL型滤波器谐振峰和内环的固有相位滞后,实现电网谐波电流的快速跟踪与高精度补偿。实验结果表明,经过并联有源电力滤波器的谐波补偿,电网电流的总谐波失真明显减小,负载变化时并联有源电力滤波器可实现单周期快速响应,验证了所提出的复合电流控制策略的有效性。     

基于PWM控制并联型APF的MATLAB仿真研究

分析了并联型有源电力滤波器的基本工作原理、系统结构和PWM控制的基本原理,采用基于瞬时无功功率理论的改进型ip-iq电流检测算法,建立了基于MATLAB仿真软件中电力系统仿真工具箱的PWM控制的并联型有源电力滤波器的仿真模型,并进行了仿真研究,给出了应用并联型有源电力滤波器抑制典型非线性谐波电流源负载对电网谐波电流污染的仿真模型,论述了连接电抗器值的大小对补偿装置性能的影响,通过仿真实验研究,结果证实了所提方案的正确性和可行性。     

并联型有源电力滤波器的无源性控制

将无源性控制应用于并联型有源电力滤波器,对有源电力滤波器电路进行简化等效．把负载电压和电源电流作为状态变量,把补偿电流作为控制变量,通过坐标变换建立有源电力滤波器的数学模型,通过无源性理论获得简单而容易实现的控制律．仿真结果表明系统具有良好的稳定性和鲁棒性。     

Decoupled control using neural network-based sliding-mode controller for nonlinear systems

In this paper, an adaptive neural network sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. The adaptive neural sliding-mode control system is comprised of neural network (NN) and a compensation controller. The NN is the main regulator controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the neural controller. An adaptive methodology is derived to update weight parts of the NN. Using this approach, the response of system will converge faster than that of previous reports. The simulation results for the cart–pole systems and the ball–beam system are presented to demonstrate the effectiveness and robustness of the method. In addition, the experimental results for seesaw system are given to assure the robustness and stability of system.     

有源电力滤波器的自抗扰控制研究

针对采用PI控制器控制有源电力滤波器时存在谐波或基波电流指令而无法实现无静差调节,且数字化后会进一步增大稳态误差,使有源电力滤波器的补偿性能降低的问题,提出一种有源电力滤波器的自抗扰控制策略。该策略利用自抗扰控制器的非线性跟踪-微分器和扩张状态观测器分别处理参考输入和输出,并选择适当的状态误差,实现了对滤波的自动、精确补偿。仿真和实验结果表明,有源电力滤波器采用自抗扰控制策略后具有很强的鲁棒性、稳定性和适应性,控制性能有较大改善。     

单相有源电力滤波器非线性统一控制策略

目前有源电力滤波器控制策略都采用电流内环和电压外环的双环控制.本文提出了一种基于精确反馈线性化的单相有源电力滤波器统一控制策略.在单相有源电力滤波器仿射非线性模型基础上,通过求解偏微分方程得到一个包含补偿电流变量和直流侧电压变量的输出函数,并推导出了其状态反馈精确线性化非线性控制律,将原非线性系统转换成微分同胚的二阶线...     

Cascade nonlinear control of shunt active power filters with average performance analysis

The problem of controlling single-phase shunt active power filters is addressed in presence of nonlinear loads. The control objective is twofold: (i) compensation of harmonic and reactive currents absorbed by the nonlinear load, this objective is referred to as power factor correction (PFC); (ii) regulation of the inverter output capacitor voltage. A two-loop cascade control strategy is developed that includes an inner-loop designed, using the Lyapunov design approach, to cope with the compensation issue and an outer-loop designed to regulate the capacitor voltage. The controller performances are formally analysed using system averaging theory. The theoretical results are illustrated by simulation.     

Observer-based adaptive control for nonlinear input-delay systems with unknown control directions

This paper investigates the problem of adaptive control for strict-feedback nonlinear systems with input delay and unknown control directions. The Nussbaum function is utilised to deal with the unknown control directions and a novel compensation system is introduced to handle the time-varying input delay. By using neural network(NN) approximation and backstepping approaches, an adaptive NN controller is designed which can guarantee the semi-global boundedness of all the signals in the closed-loop system. Two simulation examples are also given to illustrate the effectiveness of the proposed method.     

Real-time torque control using discrete-time recurrent high-order neural networks

This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high-order neural network to identify the plant model. In order to train the neural identifier, the extended Kalman filter (EKF) based training algorithm is used. The neural identifier is in series-parallel configuration that constitutes a well approximation method of the real plant by the neural identifier. Using the neural identifier structure that is in the nonlinear controllable form, the block control (BC) combined with sliding modes (SM) control techniques in discrete-time are applied. The BC technique is used to design a nonlinear sliding manifold such that the resulting sliding mode dynamics are described by a desired linear system. For the SM control technique, the equivalent control law is used in order to the plant output tracks a reference signal. For reducing the effect of unknown terms, it is proposed a specific desired dynamics for the sliding variables. The control problem is solved by the indirect approach, where an appropriate neural network (NN) identification model is selected; the NN parameters (synaptic weights) are adjusted according to a specific adaptive law (EKF), such that the response of the NN identifier approximates the response of the real plant for the same input. Then, based on the designed NN identifier a stabilizing or reference tracking controller is proposed (BC combined with SM). The proposed neural identifier and control applicability are illustrated by torque trajectory tracking for a DC motor with separate winding excitation via real-time implementation.     

Robust Adaptive Control of Robots Using Neural Network: Global Stability

A desired compensation adaptive law‐based neural network (DCAL‐NN) controller is proposed for the robust position control of rigid‐link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on‐line, with no offline learning phase required. When compared with standard adaptive robot controllers, we do not require linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications. A comparative simulation study with different robust and adaptive controllers is included.     

Adaptive neural network dynamic surface control for a class of nonlinear systems with uncertain time delays

This paper presents a solution to tracking control problem for a class of nonlinear systems with unknown parameters and uncertain time-varying delays. A new adaptive neural network(NN) dynamic surface controller(DSC) is developed. Some assumptions on uncertain time delays, which were required to be satisfied in previous works, are removed by introducing a novel indirect neural network algorithm into dynamic surface control framework. Also, the designed controller is independent of the time delays. Moreover,the dynamic compensation terms are introduced to facilitate the controller design. It is shown that the closed-loop tracking error converges to a small neighborhood of zero. Finally, a chaotic circuit system is initially bench tested to show the effectiveness of the proposed method.     

Adaptive neural control and learning of affine nonlinear systems

This paper presents deterministic learning from adaptive neural network control of affine nonlinear systems with completely unknown system dynamics. Thanks to the learning capability of radial basis function, neural network (NN), stable adaptive NN controller is designed for the unknown affine nonlinear systems. The designed adaptive NN controller is rigorously shown that learning of the unknown closed-loop system dynamics can be achieved during the stable control process because partial persistent excitation condition of some internal signals in the closed-loop system is satisfied. Subsequently, neural learning controller using the knowledge obtained from deterministic learning is constructed to achieve closed-loop stability and improve control performance. Numerical simulation is provided to show the effectiveness of the proposed control scheme.     

A novel harmonic-free power factor corrector based on T-type APF with adaptive linear neural network (ADALINE) control

A novel harmonic-free power factor correction (PFC) topology based on T-type active power filter (APF) is proposed in this paper. The proposed system has better stability characteristics compared to conventional shunt APF topologies and it is a natural filter for the non-linear load harmonic disturbances. The tuned passive filters are connected at the ac-side of the rectifier load, which are designed to provide fundamental reactive power compensation and eliminate majority of load harmonics in order to minimize the power rating and heat dissipation of the voltage source inverter (VSI). The control scheme is based on a decoupled state-space equations of the T-type APF using separate proportional-integral controllers in d-axis and q-axis of rotating reference frame synchronized with grid voltages, respectively. The fundamental components of load-side currents are feed-forwarded in the current control loop using two groups of synchronous frame adaptive linear neural networks (ADALINEs) to ensure a fast dynamic response. A proportional-integral controller is adopted in the outer voltage loop for balancing the active power flow of the dc-side capacitor of the VSI. The proposed power factor corrector topology is studied analytically and by simulation under various scenarios using Matlab/Simulink. The validity and effectiveness of the proposed topology as well as its control schemes are substantially confirmed by the simulation results.