Next Article in Journal
Electromagnetic and Calorimetric Validation of a Direct Oil Cooled Tooth Coil Winding PM Machine for Traction Application
Previous Article in Journal
SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study
Open AccessArticle

Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization

by Ruiye Li 1, Peng Cheng 1,*, Yingyi Hong 2, Hai Lan 1 and He Yin 1
College of Automation, Harbin Engineering University, Harbin 150001, China
Department of Electrical Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 320, Taiwan
Author to whom correspondence should be addressed.
Energies 2020, 13(13), 3337;
Received: 4 June 2020 / Revised: 20 June 2020 / Accepted: 26 June 2020 / Published: 30 June 2020
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. View Full-Text
Keywords: multi-physical field collaborative; multi-objective genetic algorithm (MOGA); neural network (NN); synchronous generator; Taguchi method multi-physical field collaborative; multi-objective genetic algorithm (MOGA); neural network (NN); synchronous generator; Taguchi method
Show Figures

Figure 1

MDPI and ACS Style

Li, R.; Cheng, P.; Hong, Y.; Lan, H.; Yin, H. Design Synchronous Generator Using Taguchi-Based Multi-Objective Optimization. Energies 2020, 13, 3337.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop