Optimal induction motor design by the use of NLP-approximation approach and SQP-method

In computational terms, design optimization for any electrical machine cons titutes a mixed integer programming (MIP) task because, along with continuo usly variable design parameters, which are allowed to assume any value with in a specified range, there exist parameters that may only assume discrete values; finding an optimal solution for such a combination of design variab les represents a computational challenge. If, on the other hand, for the pu rpose of searching for the optima, discrete variables could be treated as c ontinuously variable quantities, the task would be considerably simpler. Th en a range of modern optimization methods based on gradient search techniqu es could be employed in determining the search direction. This approach wou ld be tantamount to having converted the MIP problem into a nonlinear progr amming (NLP) problem. This paper describes the application of a sequential quadratic programming (SQP) technique in design optimization for an inducti on motor. It demonstrates how a MOP-problem can be successfully approached using an. NLP-approximation to simplify the task of finding optima. The des ign algorithm, implemented on a desktop computer, allows globally optimized designs to be found with relative ease, unlikely to be achievable with con ventional design methods. Aspects of algorithm implementation are discussed , including the formulation of the NLP-approximation, convergence speed, an d the nature of convergence.