异步电动机动态模型参数识别

本文应用遗传算法(GAs)来解决异步电动机动态数学模型参数识别的问题。本文详细给出了遗传算法应用于动态数学模型参数识别问题的一般步骤,利用异步电动机直接空载起动特性对异步电动机动态数学模型参数进行了识别,给出了识别参数及有关结果。     

异步电动机动态模型参数识别

本文应用遗传算法来解决异步电动机动态数学模型参数识别的问题。本文详细给出了遗传算法应用于动态数学模型参数识别问题的一般步骤，利用异步电动机直接空载起动特性对异步电动机动态数学模型参数进行了识别，给出了识别参数及有关结果。     

不同定转子漏电感比例关系对异步电动机动态特性和参数识别精度的影响

应用异步电动机动态数学模型，就不同定转子电感比例关系对电动机动态特性和遗传算法参数识别精度的影响进行了对比分析，给出了若干典型动态特性和参数识别结果。不失一般性，也给出了若干稳态特性。结论是对于提高遗传算法参数识别精度有积极作用。     

用遗传算法辨识加速度传感器动态模型参数

本文提出了一种遗传算法（ＧＡ）辨识加速度传感器动态数学模型参数的新方法。该方法以传感器时域动态性能实验数据为基础,通过ＧＡ优化来确定传感器传递函数参数。结果表明,这种方法具有较高的精度,且适用性广。     

改进粒子群算法在永磁同步电动机参数识别中的应用

应用改进粒子群优化算法(IPSO)来识别异步起动永磁同步电动机起动时的动态模型参数.永磁同步电动机异步起动时的电机动态模型采用状态微分方程表示.与遗传算法(GA)和标准粒子群算法(SPSO)相比较,仿真试验表明改进粒子群算法明显提高了识别参数的准确性,同时表明改进粒子群算法能更好地识别永磁同步电动机起动时动态模型的参数.     

基于模糊控制遗传算法的输电系统最优化规划

用遗传算法求解优化问题时,要花费大量时间对基因进行测试,,速度较慢。另外,遗传算法的性能还强烈地依赖于一睦相关参数（例如交叉和变异的概率）的选取。文中以电网规划为背景,对简单遗传算法（ＳＧＡ）进行了多方面改进,得到模糊控制遗传算法（ＦＬＣＧＡ）。该算法速度快,收敛到全局最优解的概率大。与基于传统遗传算法的电网规划比较,ＦＬＣＧＡ算有明显的优越性。     

M-T两轴标量解耦控制异步电动机的运行

为解决在通常异步电动机运行控制方法中,转子回路参数（特别是时间常数）对异步电动机的动态特性有较大的问题,提出了一种新的异步电动机控制方法——标量解耦方法。应用标量解耦方法对T-II型异步电动机特性进行分析和仿真,结果较好地消除了转子回路参数对动态特性的影响。     

混合寻优法在无功优化中的应用研究

根据遗传算法（ＧＡ）和模拟退火算法（ＳＡ）的共性和异性,利用各自的优点,提出了ＳＡ／ＧＡ、ＧＡ／ＳＡ两种混合寻优的方法,并应用于地区电网无功优化的过程。通过ＩＥＥＥ－３０节点系统的算例,给出了算法的比较结果,从而说明了混合寻优算法不仅在寻优过程上优于ＳＡ、ＧＡ算法,而且使收敛速度得到大大提高。     

基于MATLAB／SIMULINK的异步电动机建模与仿真

从异步电动机的数学模型着手介绍了一种基于ＭＡＴＬＡＢ＼ＳＩＭＵＬＩＮＫ的异步电动机仿真模型,该模型封装后可置入ＳＩＭＵＬＩＮＫ的模型库中,使用时只需调用该模型并置入相应的电机参数即可。最后通过仿真实验验证了模型的正确性。     

异步电动机优化设计的改进及应用

采用非线性规划方法对异步电动机的优化设计进行了较为深入的研究。利用“惩罚函术”法（ＳＵＭＴ）进行了数学模型的处理,并研究了该方法中诸参数的合理取值。采用并改进了Ｈｏｏｋｅ－Ｊｅｅｖｅｓ的模矢搜索法及Ｐｏｗｅｌ的共轭方向法,还提出一种随机搜索法,进行了大量的优化计算及分析研究。最后还对如何判断和获得全局最优解的问题进行了讨论并给出了优化设计实例。     

Bayesian methods for time‐varying state and parameter estimation in induction machines

This paper addresses the problem of nonlinear time‐varying state and parameter estimation of induction machines (IMs) on the basis of a third‐order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non‐Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non‐Gaussian processes without a priori state information, by utilizing a time‐varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results. Copyright © 2014 John Wiley & Sons, Ltd.     

基于PWM逆变器的直线感应电机参数辨识方法

针对直线感应电机初级漏感和次级漏感不相等的特性,提出了一种基于PWM逆变器的直线感应电机参数辨识方法,将堵转试验的等效电路改进后得到方程组计算电机的参数。将此方法应用于一额定电压30V的直线感应电机参数辨识中,试验结果与传统方法得到的试验结果比较,证明了所提出的方法提高了辨识精度。     

一种基于静态特性的直线感应电机参数辨识方法

直线感应电机(LIM)具有机械间隙大、定转子漏感差别明显、边缘效应影响等特点,准确测量电机参数难度较大.通常的电机空载试验参数测量方法在直线轨道上难于实施,特别是用于地铁和磁浮列车牵引的直线电机,由于运动位置重复性差,在线辨识实现困难.本文提出一种新的基于变频器驱动的直线感应电机等效电路参数辨识方法,利用不同频率时的电机推力、变频器输出电压和电流等静态堵转特性,结合最小二乘法推算出适合控制用的直线感应电机等效电路中的初级电阻、漏感、次级等效电阻和漏感参数.测量设备即为实际驱动电机的变频器.对一台基于直线感应电机牵引系统的测试结果表明了该方法的有效性.     

嵌入式永磁同步电机离线参数辨识技术

详细介绍了一种永磁同步电机(Permanent Magnet Synchronous Motor,简称PMSM)矢量控制系统电机参数的离线辨识技术.该技术基丁电机的动态数学模型,通过在电机端注入直流或交流电流,测量相应的电压响应,辨识出电机的定子电阻、转子磁链、直轴电感和交轴电感.实验结果证明了该算法的正确性.     

Real-time estimation of the electric parameters of an inductionmachine using sinusoidal PWM voltage waveforms

This paper studies the parameter estimation of induction machines. The problems associated with the parameter estimation of induction machines are analyzed through sensitivity functions. Then, models and a procedure to estimate a set of four basic parameters (r_s, σl_s, l_s, and τ_r) are proposed. The discrete-time parameter estimation models are written in the δ operator specifically because it provides good numerical properties at high sampling rates, where the discrete-time model approaches its continuous-time equivalent. This feature permits the direct estimation of the continuous-time parameters. The main feature of the proposed procedure is the possibility to estimate the parameters by using sinusoidal signals, and without the measurement of the machine speed. The experimental results obtained with the proposed estimation procedure are presented and demonstrate that it is possible to map the parameters in terms of the operating conditions of the induction machine     

Evaluation of Steady-State and Transient Behavior of a MRAS Based Sensorless Rotor Flux Oriented Induction Machine in the Presence of Parameter Detuning

One of the most frequently applied methods of speed-sensorless rotor flux-oriented control of induction machines relies on utilization of model reference adaptive system (MRAS)-based speed estimation. Accuracy of this method heavily depends on correct setting of the machine parameters within the estimator and the controller. The paper discusses sensorless indirect feed-forward current-fed rotor flux-orientedinduction motor drive in whichspeed estimation is performed utilizing rotor flux-based MRAS scheme. A study is conducted in order to evaluate speed estimation error and other detuning effects in both steady-state and transient operation of the drive caused by incorrect setting and/or variation of parameters. Theoretical principles behind the steady-state analysis are at first developed, and steady-state detuning effects are then evaluated. Incorrect setting of magnetizing inductance, stator resistance variation, rotor resistance variation, and leakage inductance variation are encompassed by steady-state analysis. Next, simulation of the complete drive is performed in order to examine impact of parameter detuning on transient behavior of the drive. Results obtained by simulation, using dynamic model, for any steady-state operating point are found to be in excellent agreement with results obtained by steady-state analysis, so that developed theoretical concepts are fully verified. It is shown that rotor resistance variation leads to the largest speed estimation error, whereas stator resistance variation at low speeds significantly affects accuracy of field orientation and hence dynamics of the drive.     

基于αβ坐标系模型的双馈风力发电机参数辨识

目前双馈式风力发电机(DFIG)为国内外风力发电机的主流机型。要研究大规模风电并网对电力系统可靠性和稳定性等方面产生的影响,必须要有准确的风力发电机的模型和参数。研究了双馈风力发电机的模型参数辨识方法。首先在Matlab/Simulink环境中搭建了风力发电机并网的仿真模型,得到并网的实测数据。然后选用发电机的αβ坐标系数学模型,在对模型进行可辨识性分析的基础上得到定子自感、定转子互感的表达式。最后利用遗传算法,并采用分步辨识策略,进一步辨识出转子初相位θ0角、定子自感、定转子互感、转子互感等风力发电机参数。为研究大规模风电并网等课题提供了可靠的理论依据。     

参数失配LCL型并网逆变器模型预测控制方法研究

针对LCL型并网逆变器模型预测控制过于依赖精确的数学模型且鲁棒性能差等问题,提出一种参数失配情况下的并网逆变器模型预测控制策略.根据LCL型并网逆变器的状态量建立离散参数观测器模型,实时估计电感和电容,并在线修正预测模型中的电感和电容参数,避免因外界扰动造成的参数失配对控制器带来的不利影响,增强控制器的鲁棒性能.仿真结...     

具有参数鲁棒性的三相电压源型并网逆变器模型预测电流控制

传统模型预测电流控制十分依赖精确的数学模型,然而建模存在的误差会使模型参数失配。针对模型参数失配引起的控制器鲁棒性较差问题,提出了具有参数鲁棒性的模型预测电流控制算法。通过对模型预测电流控制的工作机理进行分析,论述了电感失配对控制器的影响;根据实际电流变化量与预测电流变化量的关系建立电感观测器并划分观测区域,使预测模型具有强鲁棒性。在两电平三相电压型并网逆变器系统上对所提算法进行仿真与实验,在模型参数失配时,控制器可通过电感观测器准确跟踪实际电感值并修正模型,从而有效降低并网电流的总谐波含量,仿真和实验结果验证了该算法的可行性与有效性。