Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection

The Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use [...]

Automatic Control of Mobile Industrial Robot Based on Multiobjective Optimization Strategy

In order to solve the optimal cascade mobile path selection problem when mobile industrial robots repair network coverage holes, a cascade mobile path selection optimization method considering the number and energy of intermediate cascade nodes is proposed. By calculating the energy availability of intermediate cascade nodes, this method further obtains the energy availability and decisive energy of each path, selects the optimal cascade mobile path from the perspective of multiobjective optimization, effectively balances the energy consumption of each mobile industrial robot, makes full use of the energy of the whole network, and prolongs the survival time of the network. Simulation results show that the optimization method has higher network energy efficiency than the standard cascaded mobile method.

Approach for the Model and Parameter Selection for the Calculation of Induction Machines

The solution of multiphysical problems in the field of electrical machines is a complex task that involves the modeling of a wide variety of coupled physical domains. Different types of models and solution methods can be used to model and solve the individual domains. In this paper a procedure for the methodical selection of the most suitable model for a given multiphysics task is presented. Furthermore, an approach for the selection of the most suitable variable machine parameters for a design optimization is presented. The model selection is presented on the basis of the electromagnetic calculation of an induction machine. For this purpose, models of different value ranges and levels of detail, such as analytical and numerical ones, are considered. The approach of the model selection is explained and applied on the basis of a coupled electromagnetic-thermal simulation of an exemplary induction machine. The results show that the model selection presented here can be used to methodically determine the most suitable model in terms of its value range, level of detail and computational effort for a given multiphysical problem.

Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection

Optimization methods are increasingly used for the design process of electrical machines. The quality of the optimization result and the necessary simulation effort depend on the optimization methods, machine models and optimization parameters used. This paper presents a multi-stage optimization environment for the design optimization of induction machines. It uses the strategies of simulated annealing, evolution strategy and pattern search. Artificial neural networks are used to reduce the solution effort of the optimization. The selection of the electromagnetic machine model is made in each optimization stage using a methodical model selection approach. The selection of the optimization parameters is realized by a methodical parameter selection approach. The optimization environment is applied on the basis of an optimization for the design of an electric traction machine using the example of an induction machine and its suitability for the design of a machine is verified by a comparison with a reference machine.