Static and Dynamic Simulation of an Induction Motor Using Matlab/Simulink

Author:

Le Roux P. F.ORCID,Ngwenyama M. K.ORCID

Abstract

Industries are adequately configured with the operational devices that are required to develop induction motors. Engineers should precisely comprehend the kind of equipment that is constructed, as with every other production system, and should start by having the goal in their perspective. An adaptable simulation of an induction motor with a protective scheme is presented. The adaptable simulation assists engineers in accurately designing motors that meet all protective standards for certain purposes. This work achieved simulations of induction motors in stable and unstable conditions. An extensive study was performed to determine the optimum design of an induction motor. This paper attempts to provide engineers with a thorough grasp of the adaptable modelling of an induction motor. In this work, a direct dq0-direct axis algorithm is presented to implement both static and dynamic modelling of a three-phase induction machine due to possible faults and high-performance requirements in induction machines. The proposed algorithm was tested against several conventional methods, and it was observed that under the stable condition of the machinery, the proposed algorithm could remove any developing faults. This conserves time and minimises the labour required of the operator, which makes the proposed algorithm more efficient. Furthermore, the machine is demonstrated in a steady-state performance with respect to the current, active power, efficiency, reactive power, power factor, and speed when the torque loads range from 0 to 125% of its nominal torque. The transient behaviour of the machine was shown through the current, electromagnetic torque, electromagnetic torque versus speed, and speed under no-load, half-load (50%), and full-load (100%) conditions. Finally, the results of the proposed technique were compared to the results of the measured parameters. It was observed that when the load changed from a half load (50%) to a full load (100%), then the supply voltage was suddenly halved with the load at full load (100%). It was observed that the proposed algorithm provides accurate estimates with a deviation of not more than +/−2% from the measured parameters.

Funder

Tshwane University of Technology

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor;ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal;2024-07-24

2. Effects of Inductive Load on Photovoltaic Systems;Lecture Notes in Networks and Systems;2024

3. Machine learning-based fault diagnosis for three-phase induction motors in ventilation systems;Australian Journal of Mechanical Engineering;2023-12-05

4. Real-Time Implementation of the Luenberger Torque Observer for an Induction Motor;2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM);2023-11-28

5. Induction Motor Fault Identification using Support Vector Machine;2023 6th International Conference on Renewable Energy and Power Engineering (REPE);2023-09-15

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