Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization

Ruiye Li, Peng Cheng, Yingyi Hong, Hai Lan and He Yin
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Ruiye Li: College of Automation, Harbin Engineering University, Harbin 150001, China
Peng Cheng: College of Automation, Harbin Engineering University, Harbin 150001, China
Yingyi Hong: Department of Electrical Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 320, Taiwan
Hai Lan: College of Automation, Harbin Engineering University, Harbin 150001, China
He Yin: College of Automation, Harbin Engineering University, Harbin 150001, China

Energies, 2020, vol. 13, issue 13, 1-18

Abstract: The extensive use of finite element models accurately simulates the temperature distribution of electrical machines. The simulation model can be quickly modified to reflect changes in design. However, the long runtime of the simulation prevents any direct application of the optimization algorithm. In this paper, research focused on improving efficiency with which expensive analysis (finite element method) is used in generator temperature distribution. A novel surrogate model based optimization method is presented. First, the Taguchi orthogonal array relates a series of stator geometric parameters as input and the temperatures of a generator as output by sampling the design decision space. A number of stator temperature designs were generated and analyzed using 3-D multi-physical field collaborative finite element model. A suitable shallow neural network was then selected and fitted to the available data to obtain a continuous optimization objective function. The accuracy of the function was verified using randomly generated geometric parameters to the extent that they were feasible. Finally, a multi-objective genetic optimization algorithm was applied in the function to reduce the average and maximum temperature of the machine simultaneously. As a result, when the Pareto front was compared with the initial data, these temperatures showed a significant decrease.

Keywords: multi-physical field collaborative; multi-objective genetic algorithm (MOGA); neural network (NN); synchronous generator; Taguchi method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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