应用改进遗传算法的电力变压器优化设计

为了提高遗传算法在电力变压器优化设计中获得全局最优解的能力，对传统遗传算法的编码方案、遗传算子以及约束条件、适应值函数等方面进行改进研究，提出了一种改进遗传算法，并应用经典数学函数进行验证，结果表明改进遗传算法具有较高的寻优效率。在此基础上提出了适应于求解多目标优化的改进遗传算法，并将改进遗传算法首次应用于S9系列电力变压器的单目标和双目标的优化设计中。应用实例表明，文中提出的改进遗传算法（IGA）具有更强的全局寻优能力和更高的求解精度，对电力变压器的优化设计效果明显。

An Improved Genetic Algorithm for Optimum Design of Power Transformers

In order to enhance the ability of global searching for genetic algorithm in power transformers optimization design, some interrelated key technique problems such as encoding method, genetic operators, restrict condition, fitness function for the traditional genetic algorithm are further reformed. An Improved Genetic Algorithm (IGA) is developed. The optimal results of a representative mathematical example show that IGA has high efficiency of global searching. At the same time, a multi-objective algorithm based on IGA is studied in this paper. IGA is applied to the single and multi-objective optimum design of S9 power transformers for the first time. All the achievements in the paper are verified a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.