Cost optimization of power transformers is a complex engineering task. The design engineers have to solve this multidisciplinary, non-linear optimization problem in a very short-time. Due to the importance of the topic, numerous optimization methods have been developed in the industry to solve thisproblem. These methods generally use different, simplified two-winding transformer models, where the windings are generally modeled by their copper filling factors. These models cannot consider the eddy current losses and the temperature gradients of the windings properly, because the calculation of these quantities needs knowledge of the conductor dimensions. This paper proposes a novel method, which uses a general geometric programming search based sub-problem to determine the optimal conductor dimensions for the optimal winding shape. The proposed method considers the temperature gradients of the windings and uses FEM to determine the eddy losses of the windings. The whole optimization process is made by an evolutionary algorithm (NSGA-II)-based search. The paper presents this novel transformer optimization methodology and then illustrates it with a practical example.

中文翻译：

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基于FEM的具有进化算法和几何规划的变压器设计优化技术

电力变压器的成本优化是一项复杂的工程任务。设计工程师必须在很短的时间内解决这个多学科的非线性优化问题。由于该主题的重要性，业界已经开发了许多优化方法来解决该问题。这些方法通常使用不同的简化的两绕组变压器模型，其中绕组通常通过其铜填充系数来建模。这些模型无法正确考虑涡流损耗和绕组的温度梯度，因为计算这些量需要了解导体尺寸。本文提出了一种新颖的方法，该方法使用基于常规几何编程搜索的子问题来确定最佳绕组形状的最佳导体尺寸。所提出的方法考虑了绕组的温度梯度，并使用有限元法确定了绕组的涡流损耗。整个优化过程由基于进化算法（NSGA-II）的搜索完成。本文介绍了这种新颖的变压器优化方法，然后通过一个实际示例进行了说明。