Optimal Induction Motor Design by the Use of NLP-Approximation Approach and SQP-Method

Abstract

In computational terms, design optimization for any electrical machine constitutes a mixedinteger programming (MIP) task because, along with continuously variable design parameters, which are allowed to assume any value within a specified range, there exist parameters that may only assume discrete values; finding an optimal solution for such a combination of design variables represents a computational challenge. If, on the other hand, for the purpose of searching for the optima, discrete variables could be treated as continuously variable quantities, the task would be considerably simpler. Then a range of modern optimization methods based on gradient search techniques could be employed in determining the search direction. This approach would be tantamount to having converted the MIP problem into a nonlinear programming (NLP) problem. This paper describes the application of a sequential quadratic programming (SQP) technique in design optimization for an induction motor. It demonstrates how a MIP-problem can be successfully approached using an NLP-approximation to simplify the task of finding optima. The design algorithm, implemented on a desktop computer, allows globally optimized designs to be found with relative ease, unlikely to be achievable with conventional design methods. Aspects of algorithm implementation are discussed, including the formulation of the NLP-approximation, convergence speed, and the nature of convergence.