Cost Pattern Value Method for Local Search Algorithms Applied to Optimal FEA-Based Design of Induction Motors.

This paper presents an approach to obtain high torque density in induction motor (IM) design by optimizing the search algorithms and processes. Optimal design of electrical machines has many sub-optimal peaks and requires significant computation time. To solve this problem, this paper proposes a new cost pattern value method (CPVM) for local search algorithms employed in optimal design. The CPVM concept is an intelligent strategy that digitizes the gradient over the searching region by sharing information between neighboring points. When the algorithm converges quickly to local optimum by a normalized weight method, it provides a mechanism to escape from that region. The validity of the proposed approach was evaluated using a benchmark function, and it was applied to optimal design of a 260 kW IM to maximize torque production based on a finite-element analysis. For better accuracy, we employ slip-frequency analysis compatible with vector control for an IM numerical analysis. [ABSTRACT FROM AUTHOR]