Abstract
Reducing the losses of electric motors is an important issue that should be emphasized among the measures to be taken to meet the increasing efficiency need in the industry. In this sense, when we look at the regulations published by the EU Energy Commission, we see that the efficiency levels of electric motors are increasing day by day. Finally, regulation no. 2019/1781 published by the European Union Energy Commission came into force on 1 July 2021 and the sale of industrial induction engines under performance class IE3 is prohibited. Various meta-heuristic research algorithms are used in squirrel cage induction motors (SCIM) design, which are known to be widely used in industry, to meet European Union Energy Commission determined efficiency values. In this study, a study to optimize the design of a 185 kW SCIM was carried out for use in line voltage fan and compressor applications. In order to obtain a motor in the IE3 efficiency class, which is the highest efficiency class defined in the IEC 60,034–30-1–2014 standard, the geometric dimensions of the motor have been optimized using the developed optimization algorithm, taking into account the minimum cost/maximum efficiency criterion. The Archimedes Optimization Algorithm (FDB-AOA) based on Fitness Distance Balance was used for the optimization process. During the optimization process, the MATLAB and ANSYS Maxwell software were run simultaneously to analyze the electromagnetic performance of the motor and the objective function. In order to verify the optimum model obtained as a result of experimental studies, Finite Element Analysis (FEA) was carried out, the optimum model was verified and the analysis results were presented. The comparison results indicate that the optimum design of a 185 kW SCIM has been successfully achieved thanks to the FDB-AOA algorithm developed in this article.
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Yenipinar, B., Şahin, A., Sönmez, Y., Yilmaz, C., Kahraman, H.T. (2023). Design Optimization of Induction Motor with FDB-Based Archimedes Optimization Algorithm for High Power Fan and Pump Applications. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_29
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DOI: https://doi.org/10.1007/978-3-031-09753-9_29
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