基于DSP的高频链并联逆变器设计

介绍一种基于DSP的高频链软开关逆变器并联控制系统.该控制系统主电路采用高频链软开关拓扑结构,控制部分采用改进的PQ控制和基于电流分解的无连线并联控制相结合,从而有效的解决在非线性负载时均流效果差的缺陷.仿真波形表明该并联逆变器具有良好的性能.     

UPS无互联线并联的一种均流技术方案

本文针对UPS逆变器无互连线并联系统,结合均流的基本原理提出了均流的一种方案,并通过理论分析和仿真结果与原有PQ法进行了比较.结果表明,该方案在线性负载均流性能良好的情况下,能较好地解决非线性负载的不均流问题.     

非线性负载下逆变器并联系统控制策略研究

微电网并联逆变器接入非线性负载时出现的谐波电流,会引起逆变器输出电压畸变严重.以两逆变器并联运行模型为研究对象,提出一种综合控制策略.首先,在电压外环采用比例积分控制和重复控制串联控制方法的同时,考虑下垂控制所引起的基波频率偏移会进一步恶化输出电压质量,改进重复控制方法,实现在频率变化时对电压谐波的有效抑制.其次,由于SOGI可以抑制输入信号的谐波,将基于SOGI的虚拟复阻抗引入到电流反馈环,抑制谐波电流对虚拟复阻抗影响,有效改善功率均分效果和抑制逆变器间环流.最后,对所提出的综合控制策略进行仿真研究,验证所提控制策略的正确性和可行性.     

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。     

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。     

并联变换器均流控制策略研究

将模糊控制引入并联变换器均流控制技术中,对模糊控制规则以及选择模糊控制器输入变量的量化因子和输出控制量的比例因子进行了分析。提出了一种按中间电流自主均流方法。该方法减小了各相电流调节幅度,使得各相电流很快均衡。仿真实验结果表明,均流控制策略能够使得并联变换器具有良好的动态均流与负载均分特性,能够在大负载变化内良好运行。     

直驱小惯量表贴式永磁同步电机的转速脉动抑制

非线性电流测量误差和电压源逆变器(VSI)非线性畸变电压造成了直驱小惯量表贴式永磁同步电机(SPMSN)的转速脉动.本文将q轴非线性电流测量误差等效为扰动负载电流,提出一种复合PI(CPI)调速器抑制电机转速脉动.该调速器由传统PI调节器与偏差补偿器并联构成,偏差补偿器用以抑制非线性负载电流.同时,用分段线性函数建立IGBT关闭时间模型,并推导了VSI非线性畸变电压表达式.引入积分型模型预测控制(MPC)作为电流环控制器,利用MPC的滚动时域最优预测特性抑制VSI的非线性畸变电压,消除了零电流钳位现象.最后,通过仿真分析验证了所提控制策略的有效性.     

基于主电流控制的并联DC-DC升压变换器改进

并联DC-DC升压变换器之间的负载电流分配关系到系统可靠性;针对并联DC-DC升压变换器,提出了一种基于主电流控制的改进下垂方法;该方法利用并联DC-DC升压变换器的算法,根据下垂法的负载调节特性自适应地调整每个变换器的参考电压;与传统的下垂法不同,在所有变换器的内环控制器中使用其中一个并联变换器的电流反馈信号,以避免并联变换器控制回路的时延差异;研究结果显示,该算法保证了负载均流与下垂法的负载调节特性一致,在并联变换器参数失配的情况下对该算法进行了测试,通过Matlab/Simulink仿真验证了该算法的有效性。     

多台逆变电源并联冗余运行控制系统的研制

提出了一种分布式控制并联方案实现多台逆变电源并联控制系统,分析了逆变电源并联运行控制过程中的电压和电流特性。试验运行结果表明,各模块均流效果好,控制策略可行,达到比较理想的并联运行控制效果。     

一种基于下垂特性的逆变器并联控制改进方法

针对传统下垂特性控制存在的不足,提出基于下垂特性的改进并联控制方案。改进方案在传统下垂特性控制的基础上增加电压频率、幅值修正环,提高了逆变器并联工作性能,削弱了连线阻抗等电气参数对逆变器的影响。最后在Matlab/Simulink仿真平台对该方案进行仿真实验,结果验证了改进方案的良好性能。     

Selective compensation of voltage harmonics in grid-connected microgrids

In this paper, a novel approach is proposed for selective compensation of main voltage harmonics in a grid-connected microgrid. The aim of compensation is to provide a high voltage quality at the point of common coupling (PCC). PCC voltage quality is of great importance due to sensitive loads that may be connected. It is assumed that the voltage harmonics are originated from distortion in grid voltage as well as the harmonic current of the nonlinear loads. Harmonic compensation is achieved through proper control of distributed generators (DGs) interface converters. The compensation effort of each harmonic is shared considering the respective current harmonic supplied by the DGs. The control system of each DG comprises harmonic compensator, fundamental power controllers, voltage and current proportional-resonant controller and virtual impedance loop. Virtual impedance is considered at fundamental frequency to enhance power control and also at harmonic frequencies to improve the nonlinear load sharing among DGs. The control system design is discussed in detail. The presented simulation results demonstrate the effectiveness of the proposed method in compensation of the voltage harmonics to an acceptable level.     

Dynamic neural networks with hybrid structures for nonlinear system identification

Dynamic neural networks (DNNs) have important properties that make them convenient to be used together with nonlinear control approaches based on state space models and differential geometry, such as feedback linearisation. However the mapping capability of DNNs are quite limited due to their fixed structure, that is, the number of layers and the number of hidden units. An example shown in this paper has demonstrated this limitation of DNNs. The development of novel DNN structures, which has good mapping capability, is a relevant challenge being addressed in this paper. Although the structure is changed minorly only, the mapping capability of the new designed DNN in this paper has been improved dramatically. Previous work [J. Deng et al., 2005. The dynamic neural network of a hybrid structure for nonlinear system identification. In: 16th IFAC World Congress, Prague.] presents a new dynamic neural network structure which is suitable for the identification of highly nonlinear systems, which needs the outputs from the real system for training and operation. This paper presents a hybrid dynamic neural network structure which presents a similar idea of serial–parallel hybrid structure, but it uses an output from another neural network for training and operation classified as a serial–parallel model. This type of DNNs does not require the output of the plant to be used as an input to the model. This neural network has the advantages of good mapping capabilities and flexibilities in training complicated systems, compared to the existed DNNs. A theoretical proof showing how this hybrid dynamic neural network can approximate finite trajectories of general nonlinear dynamic systems is given. To illustrate the capabilities of the new structure, neural networks are trained to identify a real nonlinear 3D crane system.     

航天器大功率直流电源数字均流方法

针对航天器大功率直流电源系统电源模块的均流问题,提出了直流电源系统数字均流方法。该方法以供配电主控软件为均流核心,将各直流电源模块进行并联输出,通过以太网与各直流电源模块进行数据交互,采集各直流电源输出电流,通过均流算法调节直流电源输出电压,以达到电源均流的目的,此方法在现有直流电源的基础上,设计了大功率直流电源并联硬件电路,包含输入控制单元、远端补偿电路和输出控制单元,软件上设计供配电主控软件,包含均流控制功能、供配电流程功能和电源模块监控功能。通过实际硬件环境试验表明,该方法均流精度高、响应速度快、故障控制能力强,在航天电源领域具备较高的实用价值。     

A coarse-grained parallel approach for seismic damage simulations of urban areas based on refined models and GPU/CPU cooperative computing

Refined models and nonlinear time-history analysis have been important developments in the field of urban regional seismic damage simulation. However, the application of refined models has been limited because of their high computational cost if they are implemented on traditional central processing unit (CPU) platforms. In recent years, graphics processing unit (GPU) technology has been developed and applied rapidly because of its powerful parallel computing capability and low cost. Hence, a coarse-grained parallel approach for seismic damage simulations of urban areas based on refined models and GPU/CPU cooperative computing is proposed. The buildings are modeled using a multi-story concentrated-mass shear (MCS) model, and their seismic responses are simulated using nonlinear time-history analysis. The benchmark cases demonstrate the performance-to-price ratio of the proposed approach can be 39 times as great as that of a traditional CPU approach. Finally, a seismic damage simulation of a medium-sized urban area is implemented to demonstrate the capacity and advantages of the proposed method.     

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components. Shunt active filters (SAF) with current controlled voltage source inverters (CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents. However, CCVSI with traditional controllers have a limited transient and steady state performance. In this paper, we propose an adaptive dynamic programming (ADP) controller with online learning capability to improve transient response and harmonics. The proposed controller works alongside existing proportional integral (PI) controllers to efficiently track the reference currents in the d -q domain. It can generate adaptive control actions to compensate the PI controller. The proposed system was simulated under different nonlinear (three-phase full wave rectifier) load conditions. The performance of the proposed approach was compared with the traditional approach. We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison. The online learning based ADP controller not only reduced average total harmonic distortion by 18.41 %, but also outperformed traditional PI controllers during transients.      

Improved control strategy for fixed-speed compressors in parallel system

Control systems for parallel compressor trains consist of a discharge pressure, anti-surge and load-sharing regulation. Control of a parallel compressor system has proven to be challenging because the control targets usually exhibit control interactions between the different control loops. To decouple this control interference, Mitsubishi Heavy Industries has developed an advanced feed-forward control structure for parallel fixed-speed compressor systems. However, operation in the presence of an unpredictable disturbance presents a few technical challenges for this structure. Most of these problems result in poor load sharing and then operation in the recycle mode in order to protect the system from surge conditions. Moreover, an anti-surge control delay occurs when operating under a low load. To overcome these problems, an improved control structure that incorporates an additional discharge flow controller signal and a nonlinear signal calculator for anti-surge valve control is proposed. To demonstrate the benefits of the proposed method, the control performance of the conventional control structure is compared to that of the proposed control structure. The target process is a parallel fixed-speed compressor system for a fuel gas supply plant. The improved control structure allows for desired load-sharing control, which is not possible with the conventional control structure. The improved control structure manages to exit the anti-surge control region, whereas the conventional control structure activates the interlock system.     

面向分布式在线学习的共享数据方法

分布式数据流已成为现代数据驱动应用产生数据的主要形式,而局部节点的数据虽然独立存储,但彼此之间是相互关联的,因此如何高效地共享局部节点数据来构建全局学习器是分布式在线学习的关键问题.针对此问题,提出一种分布式在线学习的数据共享解决方案,包括基于指数损失的半监督聚类方法和基于协方差矩阵与均值向量的数据共享方法,并证明重构数据集的累计绝对误差小于给定绝对误差界的概率下界.实验表明:所提出的方法可以使节点间的共享数据量维持在一个较低的水平,同时保证基于重构数据训练得到的学习器具有很好的泛化学习能力.     

DC/DC变换器平均电流自动均流并联控制的研究

采用三环控制策略,其中电流环为内环,电压环和均流环并行为外环,且反馈量是输出电流与输出电压,可将功率电路视为黑匣子,方便了各模块样机的并联;最后以并联Buck变换器为研究对象,设计了各环补偿网络,且对系统进行了稳定性分析。仿真结果表明,系统的动态响应与稳定性均达到要求,抗干扰能力强,且均流效果良好。     

基于动态分界点计算的并行几何校正算法

近年来,遥感图像几何校正的并行处理成为重点研究的对象·但现有的并行算法尚存在一些问题,这些算法不具备负载平衡能力或者全局计算量大,而且局部操作非常耗时·针对以上不足提出了一种基于动态分界点计算的并行几何校正算法PI WA-DDC·通过LogP模型,推导出PI WA-DDC算法具有良好的可扩展性·通过在MPP上的测试数据,验证了该算法具有良好的负载平衡能力和高效处理几何畸变的能力·