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.
Highlights
Electrical machine is one of the most promising solution to reduce energy crisis, air pollution and global warming
Through 3-D multi-physical field collaborative model, the temperatures of three subsets are simulated and the results of the training subset are shown in Table 4, and other subset results are in Tables A2 and A3
The different methods included in the study were orthogonal array, shallow neural network, and multi-objective genetic algorithm
Summary
Electrical machine is one of the most promising solution to reduce energy crisis, air pollution and global warming. In this array, the first seven columns (A to G) that represent seven control factors are used and the redundant parts thereof are ignored (see Table A1 for orthogonal array L27 313 ). After completing the above selection, assignment, and combination of stator geometry variables, each group of experiments in Taguchi orthogonal array is simulated under the specified conditions. The variables are controlled for comparison and the experiments for each group are performed with constant output power and output voltage. A three-dimensional multi-physics field collaborative model conforming to the engineering error after comparison with experiments is used ignored (see Table A1 for orthogonal array L27313). The variables are controlled for comparison and the experiments for each group are performed
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