Numerical multi objective optimization of a squirrel cage induction motor for industrial application
CHALLENGE - Induction motors (IM) are presently the most widespread kind of electric machine in industrial applications thanks to their low cost, robustness and reasonably good power density. Competition among electrical equipment manufacturers to gain new IM market shares is very tough. In the attempt to win the challenge, one opportunity is to minimize the motor's specific power cost while respecting performance and technical constraints imposed by regulations. An effective way to achieve this is applying multi-objective optimization tools in the design stage.
SOLUTION - This study exposes how optimization has been carried out using multi-objective genetic algorithm techniques implemented into a commercial software modeFRONTIER. First, a new algorithm (NA) based on FEA simulations, analytical formulas, and T-Type equivalent circuit solution is developed and implemented to define the performance of the IM machine. The target of the optimization is the increase of the motor power density, in the full respect of given Oil&Gas industrial standards, with a decrease of the motor specific power cost (€/kW). A set of four specific price constants is introduced in order to evaluate the overall cost of the whole machine. The optimization objectives are: minimization of the ‘price per power’ ratio (€/kg) to push the algorithm to find constructive solutions that fit for the necessity of being competitive on the market and minimization of the maximum winding temperature to force the optimization towards geometry that comply with the thermal class and make the most of the material properties. The modified multi–objective genetic algorithm (MOGA II) algorithm is selected for the optimization due to a large number of input parameters and constraints/objectives.
BENEFITS - The materials cost reduction achieved with the optimized design can be regarded to be proportional to the weight specific power increase, resulting in a noticeable -17% specific power cost reduction (€/kW). This means that to deliver a given power amount the optimum design active parts are 17% cheaper than what would be necessary if using the existing design.