基于传递函数的遗传算法感应电机参数辨识

提出了一种基于传递函数的感应电机参数辨识方法,这种方法是通过计算感应电机实际输出和电气模型输出的差值来不断对估计模型进行修正,从而辨识出感应电机参数,并与传统的最小二乘算法辨识结果和电机参数真值进行了比较。实验证明,采用该方法对感应电动机进行参数辨识是可行的,辨识结果是可信的。     

遗传算法电机优化设计简介

介绍了遗传算法的基本思想及其特点,实现了基于遗传算法的电机优化设计,讨论了保证其全局收敛性的方法,最后给出了基于遗传算法的电机优化设计实例。     

运用于交流电机矢量控制的遗传算法参数辨识

针对传统的交流电机参数获取方法存在一定的缺陷,导致矢量控制系统不能实时准确地获取电机的参数的缺点,提出了利用遗传算法辨识电机参数的无速度传感器异步电机矢量控制系统。搭建了实验系统,并进行了相关实验,结果表明该矢量控制系统在启动、调速、稳态运行时具有良好的静、动态性能,表明将遗传算法参数辨识运用到交流电机矢量控制系统能大大提升系统性能。     

遗传算法在电机优化设计中的应用

在阐述遗传算法的性质和过程的基础上,指出遗传算法可以在电机优化设计这类复杂的有约束、非线性、混合离散多目标规划问题中应用。     

基于遗传算法的感应电动机稳态模型参数辨识

介绍了感应电动机传统的参数测试与识别方法,提出了基于遗传算法的感应电动机稳态模型参数辨识方法,利用实际所测的电动机电流和转速数据,结合感应电动机稳态模型电流特性方程,通过遗传算法进行曲线拟合得到电机稳态模型电流特性曲线及电机稳态模型各参数值。试验证明该设计参数辨识方法与传统测试方法相比,准确性和精度都有较大的提高,进一步验证了该设计方法的可行性和有效性。     

二相混合式步进电机模型参数的辨识

步进电机是一种高度非线性、强耦合的位置伺服执行元件 ,如何建立较为简单、准确的电机模型 ,为伺服系统的设计提供理论基础 ,一直是研究的热点。本文以一种二相混合式步进电动机模型参考矢量控制位置伺服系统的设计为背景 ,提出了一种比较简单的电机数学模型 ,采用最小二乘法和改进的遗传算法相结合的辨识方法获取模型参数 ,以保证其准确性。实验证明 ,模型是比较简单、准确的 ,能够较好地满足伺服系统实时性、准确性的要求     

永磁同步电动机参数辨识

提出了一种具有较强工程应用价值的永磁同步电动机的参数辨识方法.采用直流实验检测出定子电阻R,脉冲电压实验检测出直、交轴电感Ld、Lq,对系统施加额定力矩电流,检测出电机的机械时间常数tm,在速度驱动状态下,检测出转子磁通Ψr,最后分析了死区对参数辨识准确性的影响.在Saber Designer半物理环境下的仿真结果,说明了该方法的正确性,具有实际应用价值.     

开关磁阻电机模型参数辨识仿真研究

开关磁阻电机模型参数辨识直接决定开关磁阻电机运行性能。由于各非线性参数变量相互耦合,辨识难度较大,传统方法易陷入局部极小值,难以得到满意的控制效果。为此,提出了一种遗传蚁群算法的开关磁阻电机模型参数辨识方法。该方法首先建立开关磁阻电机模型,然后采用遗传算法对模型参数进行辩识,并将该辩识结果作为蚁群算法的初始信息素,最后采用蚁群算法对参数辩识进一步求解。仿真实验结果表明,相对于传统参数辩识方法,遗传蚁群算法能够对非线性参数进行准确的辩识,更准确有效地反映电机实际的工作状态。     

基于遗传算法的内埋式永磁同步电机参数辨识方法

针对内埋式永磁同步电机的反凸极特性及传统参数辨识方法存在的缺陷,结合电机的数学模型提出了一种基于遗传算法的参数辨识方法,该方法能同时辨识定子电阻、d轴电感、q轴电感和永磁体磁链四个参数。该方法所用的信号均为可直接检测的状态变量,从而减少了其他干扰对电机参数辨识的影响,提高了参数辨识的准确性。仿真和实验结果表明,利用遗传算法进行参数辨识鲁棒性强、收敛性好,在不同的转速、不同的负载以及不同的控制策略下,四个待辨识参数也能够在较短的时间内收敛到真实值,具有较高的精度,同时也克服了一般遗传算法对辨识参数初始值要求高的缺点。     

多轮进化遗传算法在电机优化设计中的应用

介绍了应用遗传算法进行电机优化设计的实施方法，重点介绍在电机优化设计中参数编译码，初始群体的设定，适应度函数定标，控制参数的设定等的处理方法，并针对优化搜索效率与优化精度的矛盾，提出采用“多轮进化遗传算法”，实例表明，这些处理手段是成功和有效的。     

汽车天窗电机设计参数对齿槽转矩影响及优化

提出天窗电机主要参数对齿槽转矩影响分析及其电磁优化设计模型研究.在阐述天窗电机基本结构和齿槽转矩产生机理基础上,以某汽车天窗电机为例,针对极槽配合和极弧系数设计以及转子静态偏心时的电机齿槽转矩进行计算,得到电机主要设计参数对电机齿槽转矩大小的影响规律.最后,以天窗电机齿槽转矩最小为目标,建立基于遗传算法的电机优化设计模型,获得了最优槽口宽、极弧系数和斜槽个数的设计方案,优化后的齿槽转矩明显小于原始设计方案.     

Estimation of induction motor parameters using shuffled frog-leaping algorithm

This paper introduces a shuffled frog-leaping algorithm based method to approximate the equivalent circuit parameters of induction machines from the manufacturer data, such as nameplate data and motor performance characteristics. The steady-state equivalent circuit is applied for the simulations. The circuit parameters are found as the result for the error minimization function between the estimated and maker data. The suggested algorithm solves the parameter estimation problem and surpasses the solutions reached by differential evolution, particle swarm optimization and genetic algorithms. Therefore, this algorithm can be employed in motor energy management system for bettering the overall energy savings in industry.     

Identification of the parameters of the high‐frequency equivalent circuit of PM synchronous motor based on genetic algorithm

This paper presents an identification technique of parameters of a high‐frequency equivalent circuit of permanent magnet synchronous motors (PMSM) driven by a PWM inverter. The high‐frequency oscillatory currents such as leakage current to the motor frame (common‐mode current) and high‐frequency oscillatory line current (normal‐mode current) are generated by switching instants of inverter transistors. The parameters of the high‐frequency equivalent circuit of PMSM which can simulate the oscillating current are identified by means of genetic algorithm. It is shown that the high‐frequency equivalent circuit with identified parameters can generate the oscillating current by some simulation results. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(4): 57–66, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20717     

基于混合Taguchi遗传算法的永磁同步电机优化设计

论文将混合Taguchi遗传算法(HTGA)引入到永磁电机的优化设计当中。HTGA具有Taguchi的局部寻优能力,又具有传统遗传算法(GA)的全局寻优能力,所以能更快速、更精确地寻找到问题的较优解或最优解,节省大量的计算时间,缩短研发周期。应用Taguchi方法来产生传统GA的子代个体,使更强壮的后代个体的参数特性得到遗传。本文建立了永磁电机的数学模型,将优化算法与永磁电机的基本电磁设计程序相结合,并将该优化设计程序应用于永磁电机的电磁设计中,得到了较好的优化效果。     

Optimum distribution system harmonic filter design using a genetic algorithm

This paper presents a technique for the anticipation of harmonic distortion as well as selection and placement of filters on distribution systems which have multiple harmonic sources and constantly changing characteristics caused by compensating capacitor switching and load impedance changes. The purpose is to predict and reduce harmonic voltage distortion for all buses in the system to comply with IEEE Standard 519. A genetic algorithm is used in this technique and shows itself an effective tool in optimizing a multivariable objective function.     

Identification in nonlinear systems by using an automatic choosing function and a genetic algorithm

This paper deals with an identification method based on an automatic choosing function (ACF) for nonlinear systems. A full data region or an entire domain is partitioned into subdomains and the unknown nonlinear function to be estimated is approximately described by a linear equation on each subdomain. These linear equations are smoothly united into a single expression by the ACF, and the resulting model is linear in its parameters. Hence these parameters are easily evaluated by the linear least-squares method. The subdomains and the ACF are properly determined by a genetic algorithm that has a high potential for global optimization. Numerical experiments demonstrate the effectiveness of the proposed method. © 1998 Scripta Technica, Electr Eng Jpn, 125(4): 43–51, 1998     

Identification of linear synchronous reluctance motor parameters

This paper deals with experimental methods for the identification of linear synchronous reluctance motor (LSRM) parameters. A magnetically nonlinear two-axis dynamic LSRM model is derived. This model accounts for the effects of slotting, saturation, cross-saturation, and the end effects. The parameters of the obtained model are not constant. They are given by the characteristics of the flux linkages, thrust, and friction force depending on the mover position and the direct (d) and quadrature (q) axis currents. These characteristics are determined experimentally by a controlled voltage-source inverter employing closed-loop current control in the d-q reference frame. The proposed model, experimental methods, and determined characteristics are confirmed through a comparison between the measured and calculated results. Two tests are performed: a test at the locked mover, and kinematic control at low speed. The effects of cross saturation under dynamic operating conditions and the effects of slotting can be clearly seen in the measured and calculated results.     

Contour extraction of glomeruli by using genetic algorithm for edge patching

Glomeruli extraction is a vital step in computer‐aided diagnosis systems of kidney disease. Since there are not only glomeruli but also other tissues in an image, when detecting the edges of glomeruli, lot of noises caused by other tissues will be detected at the same time. These noises cause discontinuous edges of glomeruli when some operation, such as labeling, is applied to denoise. According to this characteristic, this article proposes a contour extraction method based on genetic algorithm (GA) for edge patching. First, a Canny operator is applied to obtain the edges of glomeruli with noises. Then labeling and other operations such as dilation, thinning and cross‐point deletion are applied to markedly remove the noises. After the above operations, GA is finally used to search for optimal patching segments to join the discontinuous edges together and a closed curve with highest fitness would be able to form the contour of glomeruli. Experimental results show that the proposed method performs well for the renal biopsy images. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Tracking control of induction motor using fuzzy phase plane controller with improved genetic algorithm

This paper proposes a fuzzy phase plane controller (FPPC) using an improved genetic algorithm (IGA) for the optimal position/speed tracking control of an induction motor. The proposed optimal algorithm (IGA) is equipped with an improved evolutionary direction operator (IEDO) to enhance the traditional genetic algorithm (GA). An application example was considered to compare the proposed IGA with the GA. Computational results show that the proposed IGA is more efficient than the GA.Fuzzy membership functions, phase plane theory and the proposed IGA are employed to design the proposed controller (FPPC) for the optimal position/speed tracking control of an induction motor. The proposed FPPC has the merits of rapid response, simple designed fuzzy logic control and an explicitly designed phase plane theory. Simulated and experimental results reveal that the proposed FPPC is superior in the optimal position/speed tracking control to conventional PI controllers.