Analysis and design of electrical machines with material uncertainties in iron and permanent magnet. Issue 5 (4th September 2017)

• Record Type: Journal Article
• Authors: Li, Min; Mohammadi, Mohammad Hossain; Rahman, Tanvir; Lowther, David
• Is Part Of: Compel. Volume 36:Issue 5(2017)
• Page Start: 1326
• Page End: 1337
• Publication Date: 2017-09-04
• Subjects: Design optimization -- Robust design -- Uncertainty estimation -- Worst case analysis; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-02-2017-0110
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines. Design/methodology/approach: The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems. Findings: The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust. Originality/value: In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.