【作者】 曲春雨；

【导师】 孙昌志；

【作者基本信息】 沈阳工业大学 ， 电机与电器， 2007， 硕士

【摘要】 论文提出了一种新的混合优化算法—混沌蚁群算法，它在深水推进电机的优化设计中收到了良好的效果。优化是一种以数学为基础，用于求解各种工程问题优化解的应用技术。随着工程实际问题的复杂化，优化设计算法越来越多的受到人们的关注。电机优化设计是一个多变量、非线性、复杂的优化问题，为此研究一种快速有效的优化算法更为重要。为了寻求一种有效可行的优化算法，文中首先分析遗传算法、模拟退火法、混沌算法、蚁群算法四种典型优化算法的搜索机理和实现步骤，指出各自的特点，编程用标准函数进行验证比较。接着，着重探讨蚁群算法的特点，分析算法的主要参数对优化性能的影响。针对蚁群算法搜索时间长、易出现停滞现象一直制约着它在众多领域进一步推广应用这一缺点，文中对基本的蚁群算法做一系列改进，使其在优化的过程中能够快速找到全局最优解。改进算法通过函数验证，并用来优化永磁同步电动机的永磁体尺寸，在加快收敛速度上收到一定的效果。由于改进蚁群算法在提高全局收敛能力上的局限性，文中在对Logistic映射和Ulam-von Neumann两种典型混沌映射进行分析的基础上，分析了混沌算法的内随机性、遍历性、规律性和对初值的敏感性的特点，进而提出将蚁群算法和混沌算法进行混合。用多约束的标准函数对混合算法进行验证，函数优化结果表明混合算法比基本蚁群及其改进算法具有更高的全局收敛能力及运行稳定等优点。通过对永磁同步电动机的优化设计进一步验证混合算法的有效性。对比优化前后的工作特性曲线可以看出，在电机工作性能基本不变的情况下，电机体积有所减小，效率获得提高。文中最后将混沌蚁群算法应用于深水无刷电机的优化设计。对优化设计后的电机用有限元软件进行空载磁场分析，得到了空载情况下的气隙磁密分布，用傅立叶分解法对其进行频谱分析。更多还原

【Abstract】 A new hybrid optimum algorithm is presented in this paper, that is chaos ant colony algorithm(CACA). By using the method, good results are obtained in optimizing the deepwater thruster motor. The optimization is an application technology based on mathematics to solve different engineering optimization problems. However, more and more people pay attention to a great parallel intelligent algorithm that is needed by the practical engineering problems with complicated, constrained, multi-minimum characters. It’s necessary to develop a fast effective optimum algorithm to solve motor design problem, which is a multivariable, nonlinear and complex optimum problem.In order to obtain a feasible optimum algorithm, four algorithms’ theories, searching steps and features are discussed in the paper. These algorithms are Genetic Algorithm(GA), Simulated Annealing(SA), Chaos Algorithm(CA) and Ant Colony Algorithm(ACA). The advantages and disadvantages are pointed out through optimizing the test functions.Then the characters of chaos and ant colony algorithm are especially discussed. Because the slow convergence speed constrains the ACA’s application to many problems, a series of improvement to the basic ACA is presented in this paper. The improved ACA is used to optimize the test functions and the magnetic pole dimensions of permanent magnet synchronous motor (PMSM). Some effect is obtained in improving convergence speed.Because of the limitation of the improved ACA, Logistic mapping and Ulam-von Neumann mapping are analyzed systematically in this paper. The intrinsic stochastic property, ergodicity, regularity and sensitivity to initial values are shown in this paper. Then a hybrid algorithm of chaos and ant colony is presented, that is chaotic ant colony algorithm. The optimum solution of test function shows that the hybrid algorithm has advantages of high precision, fast convergence and stabilization. The feasibility and utility are confirmed by the optimum example of PMSM. Under the condition of unchanged working performance, the volume and efficiency are decreased and improved respectively. After establishing objective function and constrained conditions of deepwater brushless DC motor (BLDCM), the main dimension, efficiency and volume of deepwater BLDCM are optimized by using the hybrid algorithm. The no-load magnetic field is analyzed by the finite element analysis software. The result of Fourier decomposition for air-gap flux density proves the validity of the optimization.更多还原