Robust Position Observer for Sensorless Direct Voltage Control of Stand-Alone Ship Shaft Brushless Doubly-Fed Induction Generators

The aim of this paper is to investigate an adaptive sensorless direct voltage control(DVC)strategy for the stand-alone ship shaft brushless doubly-fed induction generators(BDFIGs).The proposed new rotor position observer using the space vector flux relations of BDFIG may achieve the desired voltage control of the power winding(PW)in terms of magnitude and frequency,without any speed/position sensors.The proposed algorithm does not require any additional observers for obtaining the generator speed.The proposed technique can directly achieve the desired DVC based on the estimated rotor position,which may reduce the overall system cost.The stability analysis of the proposed observer is investigated and confirmed with the concept of quadratic Lyapunov function and using the multi-model representation.In addition,the sensitivity analysis of the presented method is confirmed under different issues of parameter uncertainties.Comprehensive results from both simulation and experiments are realized with a prototype wound-rotor BDFIG,which demonstrate the capability and efficacy of the proposed sensorless DVC strategy with good transient behavior under different operating conditions.Furthermore,the analysis confirms the robustness of the proposed observer via the machine parameter changes.

Generalized Predictive Control with Online Least Squares Support Vector Machines

这份报纸基于能有效地处理非线性的系统的联机最少的广场支持向量机器(LS-SVM ) 建议一个实际概括预兆的控制(GPC ) 算法。在每个采样时期，算法递归地由增加新数据对并且在实时性质上从考虑删除最不重要的修改模型。删除的数据对被 lagrange 的绝对值从最后一个采样时期更多样地决定。当增加新数据对并且删除存在的时，纸给模型参数的递归的算法分别地，一个大矩阵的倒置被避免，存储器能被算法完全控制。非线性的 LS-SVM 模型在每个采样时期在 GPC 算法被使用。抵销过程的 pH 上的概括预兆的控制的实验显示出建议算法的有效性和实物。

A novel control strategy of a variable-speed doubly-fed-induction-generator-based wind energy conversion system

This paper aims to address the issue of control of a variable-speed wind turbine based on doubly-fed induction generators. In this work,an effort is made to extract the maximum efficiency from a doubly-fed induction generator-based variable-speed wind turbine by controlling the rotor current. In the first step, a maximum power point tracking technique is used to extract the maximum power from theturbine. Then a stator-flux-oriented vector control strategy is employed to control the rotor-side current. Subsequently, a grid voltagevector-oriented control strategy is used to control the grid-side system of the grid-connected generator. Considering the nonlinearityand parameter uncertainty of the system, an active disturbance rejection controller with a sliding-mode-based extended-state observeris developed for the above-mentioned control strategies. Furthermore, the stability of the controller is tested and the performance of thecontroller is compared with the classical proportional-integral controller based on disturbance rejection, robustness and tracking capability in a highly non-linear wind speed variation scenario. Modelling, control and comparison are conducted in the MATLAB®/Simulink®environment. Finally, a real-time hardware set-up is presented using the dSPACE ds-1104 R&D processing board to validate the controlscheme. From the result of the experiments, it is seen that the proposed controller takes 10-15 control cycles to settle to its steady-statevalues, depending on the control loop, whereas the conventional proportional-integral controller takes 60-75 control cycles. As a result,the settling time for the proposed control scheme is shorter than that of the proportional-integral controller.

Temperature prediction control based on least squares support vector machines

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.

Nonlinear decoupling controller design based on least squares support vector regression

Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control of a general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is un- known or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.

New predictive control algorithms based on Least Squares Support Vector Machines

Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.

采用改进遗传算法优化LS-SVM逆系统的外转子无铁心无轴承永磁同步发电机解耦控制

为了实现外转子无铁心无轴承永磁同步发电机(outer rotor coreless bearingless permanent magnet synchronous generator,ORC-BPMSG)的精确控制,提出一种基于改进遗传算法(improved genetic algorithm,IGA)优化最小二乘支持向量机(least square support vector machine,LS-SVM)逆系统的解耦控制策略。首先,基于ORC-BPMSG的结构及工作原理,推导其数学模型,并分析其可逆性。其次,建立LS-SVM回归方程,并采用IGA优化LS-SVM的性能参数,从而训练得到逆系统。然后,将逆系统与原系统串接,形成伪线性系统,实现了ORC-BPMSG的线性化和解耦。最后,将提出的控制方法与传统LS-SVM逆系统控制方法进行对比仿真和实验。仿真和实验结果表明:所提出的控制策略可以较好地实现ORC-BPMSG输出电压和悬浮力、以及悬浮力之间的解耦控制。

基于核递归最大总广义相关熵的时间序列预测

针对核自适应滤波器(KAF)在非高斯和脉冲噪声环境下预测性能下降问题,本文提出了一种新颖的鲁棒算法,称为核递归最大总广义相关熵(KRMTGC)算法.首先,简要介绍系统模型和最大总相关熵(MTC)准则;其次,在核空间采用灵活的总广义相关熵准则取代MTC准则,详细推导出KRMTGC算法,该算法对异常值或非高斯噪声具有更强的鲁棒性;此外,为进一步控制KRMTGC算法中核矩阵无限扩张模式,采用矢量量化思想降低计算复杂度,提出量化KRMTGC算法;然后,研究分析KRMTGC算法的局部收敛特性;最后,通过在基准Rossler系统和真实厄尔尼诺–南方涛动时间序列预测中的仿真结果表明:相比其他KAF算法,所提算法具有更优的预测速度和预测精度.

SOGI级联SFNF的高频注入无传感器电机控制方法

针对传统脉振高频电压注入法同时应用带通滤波器和低通滤波器导致相位偏移和位置估计误差大的问题,提出一种级联二阶广义积分器(SOGI)和单频陷波器(SFNF)的改进方法,实现位置误差信号的精确和实时提取。研究了脉振高频电压注入法位置观测闭环传递函数的幅频特性,利用SOGI的选频特性提取高频交轴响应电流,并利用SFNF的陷波特性滤除注入信号二次谐波,替代了传统误差信号提取环节中的带通滤波器和低通滤波器,参数整定简便,具备兼顾滤波精度和带宽的优势。搭建实验平台对传统误差信号提取策略和所提SOGI级联SFNF策略进行对比,实验结果表明,本文所提改进方法的响应速度和位置估计精度相比传统方法均有提高:在转速突变过程中转速估计误差降低5.9 r/min,转子位置误差降低0.11 rad;在突加负载时,转速估计误差降低3 r/min,转子位置误差降低0.08 rad,响应调节时间缩短42%,有效提高了位置观测精度和系统的动态响应性能。

不同控制策略下直驱风电机组的机网耦合特性及稳定性分析

直驱风电机组与弱交流电网或柔性直流输电系统间相互作用易导致宽频振荡问题。现有研究普遍认为,直驱风电机组与交/直流电网间的交互特性主要取决于其网侧变流器,故通常忽略直驱风电机组的机侧系统动态。已有文献讨论传统矢量控制下机侧系统对直驱风电机组并网特性的影响,但针对不同控制策略下直驱风电机组的机网耦合特性尚缺少相关研究。文中针对4种典型控制策略下直驱风电机组的机网耦合特性进行比较研究。首先,推导并验证了直驱风电机组在不同控制策略下的详细阻抗模型;然后,基于机网耦合度指标对直驱风电机组的机网耦合特性进行对比分析,得出不同控制策略下机侧系统动态对直驱风电机组交流侧阻抗特性及并网稳定性的影响;最后,通过直驱风电机组并弱交流电网分析案例验证了理论分析的正确性。

无刷双馈风力发电机低电压穿越控制策略研究

针对无刷双馈风力发电系统,稳态时,采用基于空间矢量调制(Space Vector Modulation,SVM)的直接功率控制(Direct Power Control,DPC)技术。在电网电压对称跌落条件下,理论分析功率绕组磁链与控制绕组电压的关系,对于故障期间控制绕组会产生较大的过电流,严重时可能损害变流器功率器件的问题,在原有的控制方案中引入前馈控制。通过将可观测的功率绕组电流进行微分运算后得到反映控制绕组反电势的直接干扰量,将其经前馈控制器引入到控制电压的参考值端,形成一种基于前馈控制的SVM-DPC复合控制。仿真结果表明,基于功率绕组电流微分前馈控制的复合控制策略可以在一定程度上抑制控制绕组过电流,能为无刷双馈风力发电机实现低电压穿越提供参考。

不平衡电压下双笼转子无刷双馈发电机的多目标控制

该文以双笼转子无刷双馈感应发电机为主要研究对象。为了解决不平衡电网电压带来的功率绕组(power winding,PW)定子绕组功率脉动和电流不平衡的问题,设计了简化矢量控制策略,对PW侧谐波功率和负序电流进行抑制。此外,通过建立控制绕组(control winding,CW)电流统一表达式,对各个控制目标之间的关系进行了数学推导和理论分析。在此基础上,建立了PW电流不平衡度约束条件下,针对PW有功功率脉动幅值和无功功率脉动幅值的多目标控制数学模型。并采用粒子群算法进行求解,对CW统一电流表达式中的系数进行优化,计算CW参考电流值从而实现多目标控制。最后在双笼转子无刷双馈感应发电机实验平台上,对所提出的控制策略的有效性和理论推导的正确性进行了验证。

双馈风机改进型三矢量模型预测电流控制

针对双馈风力发电机三矢量模型预测电流控制算法复杂度,开关频率和控制性能问题,本文提出一种改进型三矢量模型预测电流控制算法。该算法旨在降低计算复杂度,同时固定开关频率并提升控制性能。首先,采用转子电流d、q轴无差拍控制原理对第一与第二最优电压矢量进行快速矢量选择,从而提高矢量选择效率,其次,基于转子电压d、q轴无差拍控制原理分配电压矢量作用时间,降低矢量作用时间计算复杂度,最后,按照每个控制周期固定开关频率原则对电压矢量作用顺序进行优化,以固定开关频率。仿真和实验结果表明,与三矢量模型预测电流控制算法相比,该算法运行时间缩短,在固定开关频率的同时有效减小了转子电流、电磁转矩和输出功率脉动,具有良好的控制性能。

航空起发一体电机系统控制器的设计

提出了航空起发一体电机系统控制器的设计方案,采用矢量控制及弱磁扩速控制方式实现电动机起动控制,发电整流模块与起动模块分时共用MOSFET三相全桥主功率电路,采用专用芯片实现同步整流,采用BUCK电路并利用多相交错并联降低输出纹波,控制算法采用3零点3极点(3P3Z)补偿方法进行28V输出稳压控制,实现了航空发动机快速起动、永磁同步电机起动到发电的平稳转换以及28V直流电源输出。仿真研究表明系统的起动时间短、动态过程性能好,28V直流电源输出稳定,并联BUCK电路各支路均流理想,具有一定的工程应用价值。

基于光储虚拟同步机的模型预测双环协同优化控制策略

随着新能源和电力电子设备的渗透率不断提高,引起新型电力系统惯量不足和稳定性下降等问题。以光储一体机并网系统为例,提出一种基于虚拟同步机(virtual synchronous generator, VSG)的模型预测双环协同优化控制(model prediction double-loop co-optimisation, MPDC)策略。在功率外环方面,根据同步发电机转子频率特性,采用模型预测控制对不同阶段的VSG参考功率进行修正。在内环方面,选取有限集三矢量模型预测电流控制(finite control set-three vector-model predictive current control, FCS-TV-MPCC)实现准确跟踪外环输出参考电压。仿真与实验结果表明,所提控制策略能提高频率跟踪精度,且与传统控制方法相比,在负载发生突变情况下可减小系统频率超调,同时降低频率变化率,从而改善有功调频特性。

NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor

In this study, Saccharomyces cerevisiae (baker's yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However, it is very difficult to control the DOC using conventional controllers because of the poorly understood and constantly changing dynamics of the bioprocess. A generalized predictive controller (GPC) based on a nonlinear autoregressive integrated moving average exogenous (NARIMAX) model is presented to stabilize the DOC by manipulation of air flow rate. The NARIMAX model is built by an improved recursive least-squares support vector machine, which is trained by an in-place computation scheme and avoids the computation of the inverse of a large matrix and memory reallocation. The proposed nonlinear GPC algorithm requires little preliminary knowledge of the fermentation process, and directly obtains the nonlinear model in matrix form by using iterative multiple modeling instead of linearization at each sampling period. By application of an on-line bioreactor control, experimental results demonstrate the robustness, effectiveness and advantages of the new controller.

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator- and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

正十二边形矢量集的永磁同步发电机磁链预测控制

针对磁链预测问题,基于广义比例积分观测器原理设计磁链观测器,并基于此实现具有误差校正的磁链预测,提升磁链预测精度;针对备选电压矢量较少的问题,构造一种均匀对称分布的正十二边形矢量集,不仅增加可选矢量数量,而且矢量集的正十二边形分布特征有利于特定次开关谐波的消除。基于以上核心算法,最终为风电系统中PMSG设计FCS-MPFC方案,并通过实验验证所提方案具有较强的参数鲁棒性和较高的转矩控制精度。

基于虚拟同步发电机的模型预测电流控制

虚拟同步发电机技术(VSG)因可使逆变器具备同步发电机的特性,得到了广泛的研究和应用。针对虚拟同步发电机使用电压电流串级控制以及脉宽调制(PWM)控制底层的逆变器时,存在控制结构复杂和动态响应能力弱等问题,本文提出了一种基于虚拟同步发电机的模型预测电流控制策略(VSG-MPCC)。该控制策略首先获得虚拟同步发电机的输出电流,将其作为模型预测控制的参考值。其次根据系统的模型对之后的电流值进行预测,最后应用价值函数选择使两者误差最小的电压矢量组合作用于逆变器。所提控制策略省去了传统的电压电流串级控制和PWM调制环节,简化了控制结构并提升动态响应能力,提供了更好的功率和频率支撑。通过Matlab/Simulink仿真和实验验证了该控制策略的有效性。

基于机器学习的分布式发电并网保护研究

由于分布式发电的接入和退出随时可能发生,对电网的影响也更为复杂,因此为了确保分布式发电并网系统的稳定运行,提出基于机器学习的分布式发电并网保护方法。针对分布式发电并网系统中存在故障、扰动以及孤岛事件,利用机器学习算法中的支持向量机建立分类器模型,对上述事件进行分类检测;设计双重滤波的协调控制策略,提高分布式发电并网在扰动事件和故障事件下的功率稳定性;通过变系数下垂控制策略,对系统直流母线电压控制,提高系统在孤岛运行状态下的电流质量。实验结果表明,所提方法可有效控制分布式发电并网的功率,保持电流稳定,具有良好的事件识别能力和保护能力。