An Optimization Procedure for a Synchronous Reluctance Machine with Fluid Shaped Flux Barriers

A new procedure for the design and optimization of the rotor laminations of a synchronous reluctance machine is presented in this paper. The configuration of the laminations is symmetrical and contains fluid-shaped barriers. The parametrization principle is used, which executes variations in the lamination geometry by changing the position, thickness and shape of the flux barriers. Hence, the optimization procedure analyzes the various configurations through finite element simulations, by means of the communication between MATLAB and Flux 2D. In the post processing stage, the best geometry which optimizes mean torque, torque ripple, efficiency and power factor is selected. Once the best rotor configuration is defined, further investigations allow improving its performance by modifying the current angle, the stator winding and the thickness of the radial ribs.

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Magnetic equivalent circuit of synchronous reluctance machine

2016 ELEKTRO ◽

10.1109/elektro.2016.7512126 ◽

2016 ◽

Cited By ~ 3

Author(s):

Arkadiusz Dziechciarz ◽

Claudia Martis

Keyword(s):

Equivalent Circuit ◽

Synchronous Reluctance Machine ◽

Magnetic Equivalent Circuit ◽

Reluctance Machine

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Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model

Sustainability ◽

10.3390/su13084379 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4379

Author(s):

Linjie Ren ◽

Guobin Lin ◽

Yuanzhe Zhao ◽

Zhiming Liao

Keyword(s):

Low Cost ◽

Parameter Extraction ◽

Synchronous Reluctance Machine ◽

Fitness Evaluation ◽

Reluctance Machine ◽

Candidate Solution ◽

Collaborative Performance ◽

Magnetic Model ◽

Quantitative Results ◽

Cost Characteristics

In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in the parameter extraction of SynRM nonlinear magnetic model, the accuracy and robustness of the metaheuristic algorithm is restricted by the excessive dependence on fitness evaluation. In this paper, a novel probability-driven smart collaborative performance (SCP) is defined to quantify the comprehensive contribution of candidate solution in current population. With the quantitative results of SCP as feedback in-formation, an algorithm updating mechanism with improved evolutionary quality is established. The allocation of computing resources induced by SCP achieves a good balance between exploration and exploitation. Comprehensive experiment results demonstrate better effectiveness of SCP-induced algorithms to the proposed synchronous reluctance machine magnetic model. Accuracy and robustness of the proposed algorithms are ranked first in the comparison result statistics with other well-known algorithms.

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