2D Hybrid Analytical Model Based Performance Optimization for Linear Induction Motors

In this thesis the domain of double-layer, single-sided, 3-phase, integral slot winding, linear induction motor (LIM)s is analyzed. Motor meta parameters such as slots and poles are difficult to optimize since they drastically effect the configuration of the motor and require heuristic optimization implementations. A non-dominated sorting genetic algorithm II (NSGAII) was implemented with the Platypus-Opt Python library. It serves as a robust, yet flexible integration while maximizing thrust and minimizing the mass of each motor iteration. Each iteration was accurately modelled using the hybrid analytical model (HAM), producing the necessary performance parameters for the NSGAII’s objective function. Field plotting capability of the processed HAM allowed for the feasibility check on postprocessing constraints, increasing the robustness of the optimization. Validation between the HAM to finite element analysis (FEA) and HAM to the baseline proved the accuracy of the modelling algorithm within the objective function. The optimization concluded that the optimal motor had 36 slots and 4 poles within the domain of 12-54 slots and 4-12 poles, where 9 feasible motors were objectively compared. The proposed design tool lacks the ability to produce a fully functional optimized motor due to domain and complexity constraints. However, it saved significant time and effort while generating reproducible results within a constrained domain. The entire optimization was completed in 5 minutes, whereas the total time for configuring all motors via FEA within the domain took 4.5 hours, proving its worth.