Modeling and evolutionary design of permanent magnet synchronous machines
Abstract
Permanent magnet synchronous machines can be designed to obtain high efficiency and high torque density. There has been intense interest in the use of population based optimization methods such as genetic algorithms to design either part or all of the machine. This motivation comes about because of the success of these methods in exploring high dimensional search spaces in a computationally efficient manner. This work builds on the literature by setting forth a computationally efficient machine analysis appropriate for use with population based optimization methods which enables the consideration of a significantly larger search space than previous work. It is also unique in that the relative performance of different parameter encoding and objective function formulations are considered.
Degree
Ph.D.
Advisors
Sudhoff, Purdue University.
Subject Area
Electrical engineering
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