基于滑动平均滤波和滞环控制的有源电力滤波器仿真研究

文章提出了一种基于瞬时无功理论的谐波电流检测新方法,该方法采用滑动窗式平均滤波来替代传统的低通滤波器,减小了谐波电流检测中的延时问题。同时采用滞环控制来提高电流的响应速度。利用Matlab/Simulink软件中对所提策略进行了仿真研究,仿真结果证明了所提策略的有效性,电流补偿效果较好,在负载突变情况下,仍具有较快的响应速度。     

单相半桥可逆整流电路的滞环电流控制

为了保证电压型PWM整流器有较好运行性能,时单相半桥可逆整流器的电流控制进行了研究.提出了一种电流跟踪性能好的滞环电流控制方法.滞环电流控制有较快的动态响应,不需要载波信号,误差可由滞环宽度调节,该方法可使电流控制精度很高,所以对单相半桥可逆整流器的滞环控制原理进行了详细的分析,并建立了基于MATLAB环境下的仿真模型,仿真结果验证了该设计方案的正确性.     

瞬时值反馈中点箝位三电平逆变器的研究

本文研究中点箝位三电平半桥逆变器单极性SPWM调制方式,分析了其六种工作模式,给出其控制信号的逻辑生成方式。为保证输出电压稳定和系统动态性能,该逆变器采用了电压电流瞬时值反馈技术。最后研制了一台1kVA/400Hz的三电平逆变器样机,实验表明采用电压电流瞬时值控制三电平逆变器具有优良的稳态和动态性能。     

有源滤波器的新型空间矢量滞环电流控制

本文提出了一种基于矢量原理的有源滤波器的滞环电流控制新方法.该方法以复平面为实施控制的矢量空间,根据电流误差矢量和参考电压矢量的空间分布对电压矢量进行最优化切换,使得电流误差控制在设定的滞环范围以内,改善滤波器性能.而且由于电压空间矢量的引入,使得其实现简单,能有效降低开关频率,改善电流跟踪性能.试验结果证明该方法的可行性.     

基于逆变器的配电网接地故障电流补偿算法研究

本文设计了一种用于不接地配电网中容性电流补偿的三相变流器控制策略.利用希尔伯特变换将接地故障时三相电流分解为正负零序分量,建立逆变器的平均状态空间模型,并给出电流控制算法.最后通过在Matlab中建立模型仿真对所设计控制算法进行了验证.     

单相交流逆变器的设计研究

本文针对单相逆变器的设计提出了一种新的CVCF逆变器方案——内部模型法。这种方法能够非常清晰地反映逆变器的内部工作原理,能按照控制精度对逆变器的输出波形进行控制。在控制策略上寻求抑制逆变器的输出因负载的改变而导致系统输出变化的方法。这种CVCF逆变器的输出不仅可以直接接入负载,还可以并入交流电网。     

单相电流跟踪型逆变器的仿真

本文对逆变器的分类,发展趋势及其在可再生能源并网发电中的作用做了简单介绍,然后用MATLAB对电流跟踪型逆变器做了仿真,得出在合适的RL值下,输出电流很好地跟踪了给定电流的变化。     

二极管箝位式多电平逆变器拓扑结构分析与控制策略研究

本文在分析五电平逆变器的基础上,归纳和分析了多电平逆变器的拓扑结构,对多电平逆变器的通用结构进行了总结.并对多电平逆变器的电压空间矢量进行了分析和研究,最詹对二极管箝位式多电平功率变换器的PWM控制技术进行了归纳和总结.     

直流电源SPWM级联式多电平高压变频器的建模与仿真

阐述了直流电源SPWM级联式多电平逆变器工作原理,并应用于高压变频器的逆变部分,介绍了其建模和仿真过程,并与传统2H桥级联式变频器进行比较。仿真结果表明直流电源SPWM级联式多电平高压变频器具有输入功率因数高,输出波形稳定、谐波污染和损耗小、所用电力电子器件数量少的优点。     

三相电流型Z源逆变器数字逻辑脉宽调制

传统电流源(CS)逆变器用于中大型的工业应用中,与传统的电压型(VS)逆变器相比只适合电压下降的场合并且需要一个相当复杂的调制器.为了解决这种问题,本文提出了三相升降压型电流型逆变器的Z源概念,也就是Z源电流型逆变器.文章用不同的调制方法对电压型逆变器进行调制,进而推设计出合适的逻辑开关方程,控制电流源型Z源逆变器.可以用DSP和一个可编程逻辑器件实现.     

A novel direct torque control method for brushless DC motors based on duty ratio control

Conventional direct torque control (DTC) suffers from large torque ripple and nonconstant switching frequency, which are caused by the hysteresis band amplitude and the motor speed. Many methods have been proposed to tackle these problems. However, these methods are usually complicated and parameter dependent. A novel DTC method for brushless DC motors based on duty ratio control is proposed to reduce torque ripple and maintain a constant switching frequency. During each switching period, an active voltage vector and a zero voltage vector are applied. A simple and effective method implemented to calculate the duty ratio relies only on the torque error, reducing the parameter dependence. The proposed method has the advantages of conventional DTC and effectively reduces torque ripple, which improves the performance of conventional DTC. Simulation and experimental results are given to confirm the method’s validity.     

A novel trust-based false data detection method for power systems under false data injection attacks

This paper proposes a novel trust-based false data detection method for power systems under false data injection attacks (FDIAs). In order to eliminate the interference posed by false data to the power system in the state estimation process, a trust model is first established to estimate the reliability of the system bus. Then an algorithm is proposed to update the bus trust value, when all the trust value of neighbor buses at one bus node are quite low, then this bus is diagnosed as a malicious node and the false data are detected. This method guarantees that the power systems can estimate the state accurately against FDIAs based on the trust of bus. The simulations on the benchmark IEEE 14-bus, IEEE 30-bus and IEEE 57-bus test systems are used to demonstrate the feasibility and effectiveness of proposed algorithm.     

A novel controller design and evaluation for networked control systems with time-variant delays

A new delayed state-variable model for networked control systems is presented, upon which a linear quadratic regulator (LQR) is designed. A method of delays-estimation online is also given. A fuzzy logic with LQR controller is addressed for the difficulty on implementation of LQR in networked control systems (NCSs) with time-variant delays. Simulation results prove that the novel controller can make the system stable and robustly preserve the performance in terms of time-variant delays.     

A flow and routing control policy for communication networks with multiple competitive users

This paper is concerned with deriving an optimal flow and routing control policy for two-node parallel link communication networks with multiple competing users. The model assumes that each user has a flow demand which needs to be optimally selected and routed on the network links. The flow and routing control policy for each user seeks to maximize the user's total throughput and minimize its expected delay. To derive such a policy, we consider a utility function for each user that combines these two objectives in an additive fashion with preference constants that can be adjusted to reflect the user's own preferences between maximizing throughput and minimizing delay. Additionally, we provide each user with the ability to make these preferences link-dependent to reflect the user's preferences for certain links over others in the network. A condition that depends on the link capacities and preference constants is derived to guarantee the existence and uniqueness of a non-symmetric flow and routing control policy solution which satisfies the Nash equilibrium of non-cooperative game theory. The Nash equilibrium is a desirable solution for such networks because it insures that no user can improve its utility by unilaterally deviating from its Nash control policy. We discuss in detail several interesting properties of this equilibrium and in particular, its relationship to the users’ preference constants. Two illustrative examples are also presented.     

A novel technique for optimal placement of piezoelectric actuators on smart structures

The problem of positioning of actuators and sensors on smart materials has been a point of interest in recent years. This is due to the fact that in many practical applications there are limitations in space, weight, etc. of the smart structures, which make the problem of positioning more complex. In addition, it is required that the actuators/sensors have the best possible performance. The development of smart structures technology in recent years has provided numerous opportunities for vibration control applications. The use of piezoelectric ceramics or polymers has shown great promise in the development of this technology. The employment of piezoelectric material as actuators in vibration control is beneficial because these actuators only excite the elastic modes of the structures without exciting the rigid-body modes. This is important since very often only elastic motions of the structures are needed to be controlled. The purpose of this paper is to introduce a novel approach developed for optimizing the location of piezoelectric actuators for vibration suppression of flexible structures. A flexible fin with bonded piezoelectric actuators is considered in this study. The frequency response function (FRF) of the system is then recorded and maximization of the FRF peaks is considered as the objective function of the optimization algorithm to find the optimal placement of the piezoelectric actuators on the smart fin. Three multi-layer perceptron neural networks are employed to perform surface fitting to the discrete data generated by the finite element method (FEM). Invasive weed optimization (IWO), a novel numerical stochastic optimization algorithm, is then employed to maximize the weighted summation of FRF peaks. Results indicate an accurate surface fitting for the FRF peak data and an optimal placement of the piezoelectric actuators for vibration suppression is achieved.     

Determination method for power-saved driving motions of manipulators by heuristic algorithms (in case of PTP control)

This study proposed a method for suppressing torques and consumed energy of manipulators considering an effect of inertial forces in order to achieve power-saved drive. The present paper describes the case that the start and end points of a work path of a manipulator are specified and the path between the points can be determined arbitrarily, which is so-called point-to-point (PTP) control. The proposed method determines both the initial pose of a manipulator and input motions of its joints so that the torque or consumed energy is suppressed. The input motion of the joint is expressed by a motion curve that indicates the time profiles of input displacements, velocities and accelerations. The proposed method determines the motion curve as a polynomial with appropriate coefficients. Those coefficients and their values are decided by a heuristic algorithm in order to suppress the input torques or consumed energy of a manipulator. The paper actually determines the power-saved drive motion of “PUMA560” by the proposed method and reveals its effectiveness.     

A new method for fault prediction of model-unknown nonlinear system

In this paper, the subspace identification based robust fault prediction method which combines optimal track control with adaptive neural network compensation is presented for prediction the fault of unknown nonlinear system. At first, the local approximate linear model based on input-output of unknown system is obtained by subspace identification. The optimal track control is adopted for the approximate model with some unknown uncertainties and external disturbances. An adaptive RBF neural network is added to the track control in order to guarantee the robust tracking ability of the observation system. The effect of the system nonlinearity and the error caused by subspace modeling can be overcome by adaptive tuning of the weights of the RBF neural network online without any requisition of constraint or matching conditions. The stability of the designed closed-loop system is thus proved. A density function estimation method based on state forecasting is then used to judge the fault. The proposed method is applied to fault prediction of model-unknown fighter F-8II of China airforce and the simulation results show that the proposed method can not only predict the fault, but has strong robustness against uncertainties and external disturbances.     

A PI controller configuration for robust control of a class of nonlinear systems

The PI control configuration for stabilization and signal tracking of nonlinear systems is investigated. Semiglobal asymptotic stability and semiglobal practical signal tracking of the controlled system are proven using results from the theory of nonlinear singularly perturbed systems.     

A dynamic output feedback control law for elastic joint robots via feedback-passivity approach

Motivated by recent dynamic output feedback passivation results, a new set-point control law is presented for an elastic joint robot when the velocity measurements are not available. The proposed methodology designs an additional dynamics with which the parallel-connected system is feedback passive. That is, the composite nonlinear robot system has relative degree one with a new output and its zero-dynamics subsystem becomes the virtual closed-loop system with a simple proportional-derivative (PD) control law. This approach provides an alternative way of replacing the role of the velocity measurements for the PD law. With the proposed control law, the transfer function of the additional system has the form of sG(s) with a strictly positive real (SPR) G(s). Robustness analysis is also given with regard to uncertainties on the robot parameters. The performance of the proposed control law is illustrated in the simulation studies of a manipulator with three revolute elastic joints.     

A novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer for feature selection

Existing personality detection methods based on user-generated text have two major limitations. First, they rely too much on pre-trained language models to ignore the sentiment information in psycholinguistic features. Secondly, they have no consensus on the psycholinguistic feature selection, resulting in the insufficient analysis of sentiment information. To tackle these issues, we propose a novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer (GWO) for feature selection (IDGWOFS). Specifically, we introduced the Gaussian Chaos Map-based initialization and neighbor search strategy into the original GWO to improve the performance of feature selection. To eliminate the bias generated when using mutual information to select features, we adopt symmetric uncertainty (SU) instead of mutual information as the evaluation for correlation and redundancy to construct the fitness function, which can balance the correlation between features–labels and the redundancy between features–features. Finally, we improve the common Spark-based parallelization design of GWO by parallelizing only the fitness computation steps to improve the efficiency of IDGWOFS. The experiments indicate that our proposed method obtains average accuracy improvements of 3.81% and 2.19%, and average F1 improvements of 5.17% and 5.8% on Essays and Kaggle MBTI dataset, respectively. Furthermore, IDGWOFS has good convergence and scalability.