基于遗传算法的大中型异步电机优化设计

本文在遗传算法基本原理的基础上,结合异步电机优化设计的特点,建立了电机优化设计算法模型,并对传统遗传算法进行了改进。通过对大中型异步电机进行优化实例研究。表明遗传算法在电机优化设计领域是一种行之有效的全局优化方法。     

基于VS2010的永磁同步电机优化设计与分析

为解决永磁同步电机设计中磁路计算复杂、查表工作繁琐以及优化设计过程中迭代处理耗时的问题,基于Microsoft Visual Studio 2010开发工具,将改进型自适应遗传算法用于电机优化设计中,利用C++语言将电机电磁计算程序与优化算法相结合,设计出具有友好界面的永磁电机优化设计软件,快速准确地找到电机设计的最优解,缩短了开发周期,大大提高了电机优化设计的效率;通过有限元软件Ansoft对优化前后的电机设计方案进行静态和瞬态的仿真分析,仿真结果表明了该电机优化设计软件的有效性和实用性。     

应用于新能源电动汽车的永磁辅助同步磁阻电机设计

以一款商业化的电机为基准,以基于有限元法的遗传算法为优化设计工具,并使用结构力学有限元校核设计了转子内薄弱区域结构,最终优化设计得到一款各方面性能均优异的永磁辅助同步磁阻电机。通过对比新设计电机与基准电机的效率云图和运行特性曲线,证实了新电机是一款可以直接替代基准电机应用的极具竞争力的新产品。     

基于NSGA-Ⅱ的无刷直流电动机优化设计

将改进型非支配排序遗传算法(NSGA-Ⅱ)应用于无刷直流电动机优化设计中,缩短电机设计周期,提高电机设计智能化程度.以效率和功率密度最高为目标,构建了基于NSGA-Ⅱ的电机多目标优化模型,采用VS2010开发平台进行了优化软件的开发,进行了一台50 W无刷直流电动机的优化设计.采用有限元分析软件Magnet验证了该算法在电机优化设计中的有效性.     

基于改进遗传算法的永磁同步电动机优化设计

由于永磁体在永磁同步电动机成本中占有一定比例,在保证电机性能指标的前提下,对永磁体体积进行优化.同时介绍了改进遗传算法在电机优化设计中的应用,并对采用基本遗传算法和改进遗传算法的优化设计结果进行比较,得出了改进遗传算法寻优收敛速度较快,优化结果精度较高的结论.     

基于遗传算法的直线感应电动机力能指标优化设计

介绍了遗传算法的基本原理,提出电机设计中约束条件的一种简化处理方法,并结合标准遗传算法对一台扁平型双边直线感应电动机进行了优化设计,优化设计后,电机的力能指标有显著提高.     

遗传算法在永磁直线同步电动机设计中的应用

水磁直线同步电动机是一种结构新颖，具有广阔发展前景的新型电机，但其电磁场计算比较复杂，因而电机优化设计也比较困难。文中在对电机内电磁场分析、计算的基础上，利用新兴的遗传算法并将其改进，应用到水磁直线同步电动机的优化设计中。实验结果表明，基于遗传算法的水磁直线同步电动机优化设计方法得到了令人满意的结果，为水磁直线同步电动机的分析和设计开辟了一个新的途径，对水磁直线同步电动机的研究及应用具有重要的意义。     

在于遗传算法的大中型异步电机优化设计

本在遗传算法基本原理的基础上,结合异步电机优化设计的特点,建立了电机优化算法模型,并对传统遗传算法进行了改进。通过对大中型异步电机进行优化实例研究。表明遗传城电机优化设计领域是一种行之有效的全局优化方法。     

基于基因控制遗传算法的永磁同步电动机优化设计

介绍了基因控制遗传算法的基本原理和寻优策略,建立了永磁同步电动机的优化数学模型,并结合基因控制遗传算法对一台钕铁硼永磁同步电动机样机进行了优化设计,得出了适应度值随进化代数的变化曲线,电机的有效材料成本显著下降,优化结果表明基因控制遗传算法应用于电机优化设计中能取得较好的效果.     

基于响应面法的三相开关磁阻电机阶梯气隙的优化设计

开关磁阻电机的高转矩脉动影响了该种电机的直驱应用,本文以直驱电动工具为应用背景,对开关磁阻电机的阶梯气隙进行优化设计,以降低其转矩脉动。首先给出了开关磁阻电机基于均匀气隙的电磁设计方案,以该方案为基础,研究了电机转矩脉动、单位相电流所产生平均转矩值随阶梯气隙的变化关系,然后将响应面法与遗传算法相结合对开关磁阻电机的阶梯气隙进行了优化设计研究,最后进行了样机试制、样机性能测试实验及样机的实际电动工具工作实验,实验结果表明了样机电机的良好性能及阶梯气隙优化设计的效果。     

现代电机优化设计纵横谈

电机优化设计是电气工程领域的热点问题之一.本文针对传统电机优化设计存在的主要问题,详细讨论了以遗传、免疫算法为代表的几种最新全局优化方法在电机优化领域的应用情况,阐述了它们不同于传统优化算法的特点,并提出了今后电机优化设计的发展方向.     

Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem

This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction phase of the genetic algorithm. A simulated annealing method is used to accelerate the convergence of the genetic algorithm by applying the simulated annealing test for all the population members. A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. The fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the proposed algorithm, a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum, and the premature convergence of the genetic algorithm. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results showed the superiority of the solutions obtained compared to genetic algorithms, tabu search and simulated annealing methods, and to two exact algorithms     

A new genetic-based tabu search algorithm for unit commitment problem

This paper presents a new algorithm based on integrating the use of genetic algorithms and tabu search methods to solve the unit commitment problem. The proposed algorithm, which is mainly based on genetic algorithms incorporates tabu search method to generate new population members in the reproduction phase of the genetic algorithm. In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. A fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the algorithm, a simple short term memory procedure is used to counter the danger of entrapment at a local optimum by preventing cycling of solutions, and the premature convergence of the genetic algorithm. A significant improvement of the proposed algorithm results, over those obtained by either genetic algorithm or tabu search, has been achieved. Numerical examples also showed the superiority of the proposed algorithm compared with two classical methods in the literature.     

Cogging Torque Reduction and Optimization in Surface-mounted Permanent Magnet Motor Using Magnet Segmentation Method

One of the main challenges in permanent magnet electrical machine design is cogging torque reduction. In this article, the magnet segmentation method is used for cogging torque reduction. For this end, each surface permanent magnet is divided into eight parts, and a symmetrical structure with equal angular widths and considering the angular gaps between them is used for minimizing a number of optimization parameters. In this article, three optimization algorithms—response surface methodology, genetic algorithm, and particle swarm optimization—are used to determine the optimal values of optimization parameters. Finally, the result is obtained that the optimum values of response surface methodology are more efficient than of those of the genetic algorithm and particle swarm optimization in cogging torque reduction, because the objective function of the response surface methodology is cogging torque that is calculated using the finite-element method, whereas the objective function in the genetic algorithm and particle swarm optimization is based on the analytical methods. However, the main objection of the magnet segmentation method is the simultaneous reduction of average torque with cogging torque.     

基于遗传算法的超导磁储能装置H2/H∞鲁棒控制器设计

提出了应用GA（遗传算法）设计H2／H∞鲁棒控制器的一般方法，使得在满足闭环系统的H∞范数小于指定的工程要求的条件下误差信号的H2范数最小，该问题可归结为一个带约束条件的极小值优化问题，并通过一种改进的遗传算法加以解决，该设计方法能对具有任意控制器结构的控制系统进行参数优化，与传统的鲁棒控制器设计方法相比，所提出的方法不仅可以提高控制器的鲁棒性，还具有易于进行参数优化的优点，将所提出的方法应用于超导磁储能装置（SMES）PI型鲁棒控制器的设计，时域仿真结果表明所设计的控制器具有很好的鲁棒性，在不同的运用工况下均能有效阻尼电力系统的振荡，此外，控制器还具有结构简单，易于工程实现的优点。     

Optimal allocation of capacitors in unbalanced multi-converter distribution systems: A comparison of some fast techniques based on genetic algorithms

Genetic algorithms (GAs) are widely used for optimal allocation of capacitors in distribution systems. When dealing with large-scale systems, such as in case of unbalanced multi-converter distribution systems, these algorithms can require significant computational efforts, which reduce their effectiveness. In order to reduce processing time for GAs and simultaneously maintain adequate levels of accuracy, methods based on the reduction of the search space of GAs or based on micro-genetic algorithms have been proposed. These methods generally guarantee good solutions with acceptable levels of computational effort. In this paper, some fast, GA-based methods are compared and applied for solving the problem of optimal sizing and siting of capacitors in unbalanced multi-converter distribution systems. The algorithms have been implemented and tested on the unbalanced IEEE 34-bus test distribution system, and their performances have been compared with the performance of the simple genetic algorithm technique.     

基于遗传算法的改进谐波平衡算法

针对标准谐波平衡算法存在迭代收敛慢,计算量大的问题,采用遗传算法所具有的隐含并行性、全局解空间搜索特性,以及其优化过程不受限制性条件约束的优点,提出了引入遗传算法加速其收敛过程的改进谐波平衡算法。通过将遗传算法的随机自适应优化过程与标准谐波平衡算法的迭代运算过程相结合,简化了标准谐波平衡算法迭代计算的过程,提高标准谐波平衡算法的收敛速度和计算精度。并采用该算法对Duffing-VanDerPol方程的进行了求解,得到了该方程的近似解析解,通过与Runge-Kutta法所得数值解相比较,两者的一致性较好,表明了该算法的有效性。     

应用贝叶斯网络模型的电力系统故障诊断

遗传算法应用于电力系统故障诊断的一个难题是如何建立合理的数学模型。针对这一难题,建立了基于元件的贝叶斯网络故障诊断模型,并通过一定的推理规则,根据贝叶斯网络形成遗传算法的目标函数,用遗传算法进行优化求解。在应用遗传算法时,对传统算法进行了一系列的改进,改善了算法的收敛性能,提出了在迭代过程中推测不完备信息的方法,增强了算法对于大量不完备保护信息的处理能力。大量算例表明了所述方法的合理性和实用性。     

现代启发式算法及其在输电网络扩展规划中的应用

对遗传算法、模拟退火法、Tabu搜索法,蚂蚁算法和粒子群算法等具有代表性的现代启发式算法的发展、特点及其比较和在输电网络扩展规划中的应用进行了总结和综述,提出了对现代启发式算法改进的三种思路,以及一些尚待深入研究的工作。     

激光粒度测试的非独立反演算法的研究

在光散射法测量微粒体系粒度分布中,关键是反演算法的选择及其正确使用。基于米氏散射理论,研究了非独立反演算法,把遗传寻优算法引入到非独立算法中。实验验证表明,引入遗传寻优到非独立反演算法扩大了非独立反演算法的寻优范围及寻优速度,对标准粒径的反演结果误差在1%以内。