Abstract：This paper addresses the transformer design optimization(TDO) problem which is a complex mixed integer nonlinear problem. Firstly, the enumeration method, which is the most robust method to find the global optimal solution, is researched to solve TDO problem. The low computation efficiency makes the enumeration method difficult to be used in industry. Quantum particle swarm optimization(QPSO) is then introduced to improve the computation efficiency, and meanwhile a new constrain handling method is proposed for multi-constrains problems. Based on the calculated results of the enumeration method, the control parameter of QPSO is analyzed under both fixed value strategy and linear variation strategy. According to the calculated results, some conclusions concerning the selection of the control parameter are drawn. The case study shows that the computation efficiency of QPSO is far better than the enumeration method. Meanwhile QPSO keeps an excellent ability to find the optimal solution.
潘再平, 张震, 潘晓弘. 基于QPSO算法的电力变压器优化设计[J]. 电工技术学报, 2013, 28(11): 42-47.
Pan Zaiping, Zhang Zhen, Pan Xiaohong. Optimal Design of Power Transformers Using Quantum-Behaved Particle Swarm Optimization. Transactions of China Electrotechnical Society, 2013, 28(11): 42-47.
[1] 中国统计年鉴[R]. 中国国家统计局. 2012. [2] Jabr R A. Application of geometric programming to transformer design[J]. IEEE Transactions on Magnetics, 2005, 41: 4261-4269. [3] Amoiralis E I, Georgilakis P S, Tsili M A. Design optimization of distribution transformers based on mixed integer programming methodology[J]. Journal of Optoelectronics and Advanced Materials, 2008, 10(5): 1178-1183. [4] 兰志勇, 杨向宇, 王芳媛, 等. Taguchi方法在内嵌式正弦波永磁同步电机优化设计中的应用[J]. 电工技术学报, 2011, 26(12): 37-42. [5] Amoiralis E I, Georgilakis P S, Tsili M A, et al. Global transformer optimization method using evolutionary design and numerical field computation [J]. IEEE Transactions on Magnetics, 2009, 45(3): 1720-1723. [6] 王竹荣, 崔杜武, 张毅坤, 等. 基于遗传算法的整流变压器的优化设计[J]. 电工技术学报, 2004, 19(5): 6-9. [7] 汪光阳, 周谦之. 基于遗传算法的异步电动机模糊控制器优化设计[J]. 电工技术学报, 2001, 16(1): 60-63. [8] Amoiralis E I, Tsili M A, Georgilakis P S, et al. Ant colony solution to optimal transformer sizing problem[C]. IEEE EPQU, 2008: 1-6. [9] Dos Santos Coelho L, Mariani V C, Da Luz M V F, et al. Novel Gamma differential evolution approach for multiobjective transformer design optimization[J]. IEEE Transactions on Magnetics, 2013, 49(5): 2121-2124. [10] Subramanian S, Padma S. Optimal design of single phase transformer using bacterial foraging algorithm [J]. International Journal of Engineering Science, 2011, 3: 2667-2684. [11] Sun J, Xu W, Feng B. A global search strategy of quantum-behaved particle swarm optimization[C]. IEEE Conference on Cybernetics and Intelligent Systems, 2004: 111-116. [12] Amoiralis E I, Georgilakis P S, Tsili M A, et al. Global transformer optimization method using evolutionary design and numerical field computation [J]. IEEE Transactions on Magnetics, 2009, 45: 1720-1723. [13] Georgilakis P S. Genetic algorithm model for profit maximization of generating companies in deregulated electricity markets[J]. Applied Artificialintelligence, 2009, 23(6): 538-552. [14] Sun J, Xu W, Liu J. Parameter selection of quantum- behaved particle swarm optimization[J]. Advances in Natural Computation, 2005, 436: 543-552.
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