A Generalized Mathematical Model to Predict Core Loss of 15kw 3Φ-Squirrel Cage Induction Motor at Various Load for Industrial Applications

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Authors

  • S. Sachin
     Department of Electrical Engineering, M S Ramaiah University of Applied Sciences, Bangalore ,IN
  • Krishnan Manickavasagam
     Department of Electrical and Electronics Engineering Education, National Institute of Technical Teachers Training and Research, Bhopal - 462 002, Madhya Pradesh ,IN
  • A. T. Sriram
     Department of Automotive and Aeronautical Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru - 560054, Karnataka ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/43608

Keywords:

Core Loss, Finite Element Method (FEM), Squirrel Cage Induction Motor (SCIM).

Abstract

The demand for effective operation of induction motor has become necessary in industrial, domestic, manufacturing, excavation, mines and many other applications. Losses estimation of the Induction Motor play a very important role in analyzing the electrical and thermal performances. Traditionally copper loss is estimated using load current and core losses is assumed constant, as it is difficult to measure core loss. An additional sensor must be place in air gap of the motor to measure core loss, increasing the complexity and uneconomical. Hence in this paper, a Finite Element Method (FEM) is used to estimate the core losses on 3ɸ - 15kW Squirrel Cage Induction Motor (SCIM) at various load conditions. A generalized mathematical model is needed to predict the core loss of the motor at various load conditions. Hence a generalized mathematical model proposed using regression technique is validated with conventional approach.

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Published

2023-12-30

How to Cite

Sachin, S., Manickavasagam, K., & Sriram, A. T. (2023). A Generalized Mathematical Model to Predict Core Loss of 15kw 3Φ-Squirrel Cage Induction Motor at Various Load for Industrial Applications. Journal of Mines, Metals and Fuels, 71(12A), 312–325. https://doi.org/10.18311/jmmf/2023/43608

Issue

Volume 71, Issue 12A , 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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