Torque and Loss Optimized Rotor Bar Design for an Induction Machine Using a Nondominated Genetic Algorithm Through Objective Function Modeling

Abstract

Induction machines are a popular choice for tractive applications due to inherent cost savings and performance benefits driving industry to search for an optimal rotor bar design. Induction machines suffer from low torque densities due to larger size and increased losses incurred in the rotor bars making these the performance objectives to be improved through optimization. Communicating through objective functions (OFs), a performance model to rapidly evaluate design parameters coupled with genetic algorithm (GA) can be used to produce an optimal rotor bar; however, conventional OF modeling may introduce function bias or complex coefficient calculations leading to dominated objectives, stalling and premature convergence leading to an unoptimized solution. In this paper, the rotor bar of a squirrel cage induction machine (SCIM) is modeled by a permeance based equivalent circuit model (ECM) creating a link between the rotor slot geometry and equivalent circuit parameters. The model considering skin and slotting effect as well as slot, zigzag, tooth top and overhang leakage reactance effects coupled with a multi- objective GA through novel hyperbolic tangent based OFs to optimize the rotor bar geometry. The optimal rotor bar shape proposed offers increased output torque and reduced total machine losses resulting in a higher operating efficiency.