Document Type
Conference Proceeding
Language
eng
Publication Date
2016
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Source Publication
2016 IEEE Energy Conversion Congress and Exposition (ECCE)
Source ISSN
9781509007370
Abstract
In this paper, a numerical technique is developed for sensitivity analysis of active material cost (AMC) in PM motors with distributed and fractional slot concentrated windings. A comprehensive analysis is carried out to identify how the optimal design rules and proportions of IPM motors with sintered NdFeB magnets vary with respect to the changes in the commodity prices of permanent magnet material, copper, and steel. The sensitivities of the correlations between the design parameters and the AMC with respect to the commodity price ranges are investigated based on response surface methodology (RSM) and large-scale design optimization practice using differential evolution (DE) optimizer. An innovative application of artificial neural network (ANN)-based design optimization is introduced. Multi-objective minimization of cost and losses is pursued for an overall of 200,000 design candidates in 30 different optimization instances subjected to different cost scenarios according to a systematic design of experiments (DOE) procedure. An interesting finding is that, despite common expectations, the average mass of steel in the optimized designs is more sensitive to changes in the commodity prices than the masses of copper and rotor PMs.
Recommended Citation
Fatemi, Alireza; Ionel, Dan M.; Demerdash, Nabeel; Stretz, Steven J.; and Jahns, Thomas M., "RSM-DE-ANN Method for Sensitivity Analysis of Active Material Cost in PM Motors" (2016). Electrical and Computer Engineering Faculty Research and Publications. 284.
https://epublications.marquette.edu/electric_fac/284
Comments
Accepted version. Published as part of the proceedings of the 2016 IEEE Energy Conversion Congress and Exposition (ECCE). DOI. © Institute of Electrical and Electronics Engineers (IEEE). Used with permission.