Design Optimization of Three Phase Squirrel Cage Induction Motor Using Evolutionary Algorithm

2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) Pub Date : 2019-03-01 DOI:10.1109/ICRAECC43874.2019.8995153

T. Sivakumar, S. Sankarakumar, T. Mahalakshmi, P. Hemalaxmi

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Abstract

The mathematical design of motor consist of nonlinear expressions, several constraints such as core saturation, thermal limits, etc., makes the manual design of motor more complex and obtaining better design is not possible with conventional design methods. To overcome this difficulty evolution algorithm based computer simulations can be used to get better performance motors. This paper discusses the application of Gravitational Search Algorithm (GSA) and Real- Coded Genetic algorithm (RGA) for optimal design of three- phase squirrel cage induction motor. The motor design is optimized using efficiency and material cost as the fitness function. The optimally designed induction machine is compared with the design developed using modified differential evolution based induction motor design and found GSA based motor design provides a better design with maximum efficiency and minimum losses. Computer simulations were carried out using MATLAB 2017a.

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基于进化算法的三相鼠笼式异步电动机设计优化

电机的数学设计是由非线性表达式和磁芯饱和、热极限等约束组成的，这使得电机的人工设计更加复杂，用传统的设计方法无法获得更好的设计。为了克服这一困难，可以使用基于计算机模拟的进化算法来获得性能更好的电机。讨论了重力搜索算法(GSA)和实数编码遗传算法(RGA)在三相鼠笼式异步电动机优化设计中的应用。以效率和材料成本为适应度函数对电机进行优化设计。将优化设计的感应电机与基于改进差分进化的感应电机设计进行了比较，发现基于GSA的感应电机设计具有效率最大化和损耗最小的优点。利用MATLAB 2017a进行计算机仿真。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

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