Optimizing a PMSM With Multi-Physics Approach for Fly-Gen Type Airborne Wind Energy Systems | IEEE Journals & Magazine | IEEE Xplore

Optimizing a PMSM With Multi-Physics Approach for Fly-Gen Type Airborne Wind Energy Systems


The figure illustrates the Pareto-front result set resulting from the solution of the machine's total weight and efficiency objective functions using a genetic algorithm ...

Abstract:

In recent years, considerable research efforts have been invested in airborne wind energy systems (AWES) due to traditional wind turbine technologies’ technical and econo...Show More

Abstract:

In recent years, considerable research efforts have been invested in airborne wind energy systems (AWES) due to traditional wind turbine technologies’ technical and economic limitations. This study describes the design process of a multimode electrical machine for a Fly-Gen AWES, employing a multi-physics approach analytical model with a multi-objective genetic algorithm. The design process integrates an analytical electromagnetic model for a permanent magnet synchronous machine (PMSM) within a multiphysics framework, incorporating thermal and mechanical constraints. To enhance the power density, the design utilizes a cobalt-iron magnetic material. The proposed method is evaluated with various constraints to validate its versatility successfully. Subsequently, a 2.5 kW motor/generator unit is optimized under a 600 V DC voltage condition and verified with 2D finite element method (FEM) analysis. The optimized machine achieves a power density of 1.8 kW/kg with an efficiency of 96%. Thermal analysis confirms that the winding and magnet temperatures remained below the critical temperature thresholds in successive motor and generator operations. Similarly, mechanical strength analysis results in satisfying mechanical limits. These findings highlight the potential of the proposed efficient and reliable machine design method.
The figure illustrates the Pareto-front result set resulting from the solution of the machine's total weight and efficiency objective functions using a genetic algorithm ...
Published in: IEEE Access ( Volume: 12)
Page(s): 66281 - 66295
Date of Publication: 08 May 2024
Electronic ISSN: 2169-3536

Funding Agency:


References

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