A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models

Abstract

Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison with a conventional heuristic approach based on a genetic algorithm directly coupled to finite element analysis assesses the superiority of the proposed approach. Finally, a sensitivity analysis on assembling and manufacturing tolerances proves the robustness of the proposed design method.

Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources

“…Permanent magnet synchronous machines (PMSMs) are widely employed in several applications such as industrial servo drives [ 1 ], electric vehicles [ 2 ], wind power generators [ 3 , 4 ], and aeronautical systems [ 5 ]. To enhance performances while predicting faults and maintenance operations, parameter identification of PMSMs represents a well-established research area [ 6 ].…”

A Regularized Procedure to Generate a Deep Learning Model for Topology Optimization of Electromagnetic Devices

“…Recently, many research areas have benefited from the potential offered by deep learning (DL) tools, such as convolutional neural networks (CNNs) [1,2]. In fact, recent works on the DL-assisted analysis of electromagnetic (EM) field computation problems showed the promising potential of CNN applications [3][4][5][6][7][8][9][10][11][12][13][14][15].…”

“…A comprehensive review of recent works on machine learning for the design optimization of electromagnetic devices can be found in [4], where the growing interest of the community for DL is clearly evidenced. Some works have adopted DL models to predict the key performance indicators of electrical machines [6,8,9,15], whilst others have focused on topology optimization [10][11][12][13].…”

A Five-Level Converter in a Three-Level Mode for Common-Mode Leakage Current Suppression in PV-Generation Systems

“…The most important part of PES is a power semiconductor converter. Increasing the installed power of a PV power generation system (PVPGS) leads to increasing power of a semiconductor converter that is an integral part of the system [2][3][4][5][6][7][8][9][10]. For these reasons, multilevel semiconductor converters are typically used.…”