Steady and dynamic states analysis of induction motor: FEA approach

Abunike, Emmanuel C.; Okoro, Ogbonnaya I.; Umoh, Gideon

doi:10.4314/njt.v36i4.29

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…The selection of material relies on its mass and cost of it. The structure of IM is designed using ANSYS with optimized specifications like dimensions, core, and winding material [41]. Carbon steel 1008 as core and Cu as winding material provided better efficiency, torque, power factor, and slip performance in our previous study [38].…”

Section: Design and Analysis Of Im Using Feamentioning

confidence: 99%

Design and Fault Diagnosis of Induction Motor Using ML-Based Algorithms for EV Application

Aishwarya

Brisilla

2023

IEEE Access

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The need for alternate transportation is driven by the increased fossil fuel cost and the adverse effects of climatic change. Electric vehicles (EVs) are the best option as they have less carbon footprint and reduced dependency on fossil fuels. Prodigious efforts to enhance the efficiency of EVs resulted in the development of highly efficient three-phase induction motors. Difficulties in designing highly efficient induction motors (IM) with high torque and power factors hindered the success of EV applications. Hence, our aim is to diagnosis fault in the designed IM under variable load conditions. The proposed EV motor is designed for 415V, 50Hz, and 5HP output power rating using ANSYS RMxprt simulation software. A fault detection strategy is also implemented with various machine learning (ML) techniques like Support Vector Machine (SVM), K-nearest neighbors (k-NN), ML perceptron (MLP), Random Forest (RF), Decision Tree (DT), Gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and Deep Learning (DL) for both healthy and faulty conditions. Short Circuit (SC), High Resistance connection (HRC), and Open-Phase circuit (OPC) are considered as faulty states for this study. Motor performance with variable load for all the states healthy and faulty are evaluated through machine learning.

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Section: Design and Analysis Of Im Using Feamentioning

confidence: 99%

Design and Fault Diagnosis of Induction Motor Using ML-Based Algorithms for EV Application

Aishwarya

Brisilla

2023

IEEE Access

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“…Three types of steel were used, after hardly obtaining the ( − ) curve and core losses curve for each of them separately and applied each of them to the three-phase induction motor in program Ansys Maxwell (transient mode) [22], [23]. By designing the motor and entering the data of curves into the program after applying the type of steel and obtained the results of iron core losses with copper losses and assessment the performance of the motor in each type of steel.…”

Section: Core Loss (Iron Loss)mentioning

confidence: 99%

Comparison and assessment of a different steel materials based on core losses reduction for three-phase induction motor

Yassin

Hussain

Ahmed

2021

IJPEDS

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This paper presents a core losses and performance calculation with different type of steel materials in the core design for three-phase induction motor by using "ANSYS Maxwell" program in order to identify the core material that provides the most effective performance by iron losses reduction. The coefficients of core losses are calculated from the magnetization curve and core Loss curve based on the on steel material databases. Although the difficult to obtain because of the little of existing information. Results show the capability of the proposed Cobalt steel (Hiperco 50) to achieve the significant losses reduction in comparison to the Electrical Steel NGO–AK Steel’s M-19 and Low Carbon Steel-SAE1020.

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“…The major challenge in optimising a model is effectively reducing cost, material and weight while improving strength performance, and life of components without compromising the effectiveness [18]. It is therefore appropriate to deploy finite element methods in solving linear and non-linear field problems including simple and complex geometries [19]. There have been various investigations on different appropriate container design and behaviour analysis of containers using FEA by several authors [20].…”

Section: Introduction Abdulrahman and Yahayamentioning

confidence: 99%

Static fatigue analysis on the load container of a low-cost vehicle for Nigerian rural farmers

Yahaya

Abdulrahman

2018

Nig. J. Tech.

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Static fatigue analysis was done on the container of a model low cost vehicle for Nigerian rural farmers to determine how well the container has been designed and its resistance to notable deformation or damage, based on the load it was subjected to. The static analysis performed involved the fixing of the container in areas where it is made to rest on the chassis. Finite element analysis of the vehicle container was conducted in two loading conditions. The result from the analysis gave a factor of safety of 4.36 for the load container at a maximum displacement of 0.131 mm and a factor of safety of 4.65 for the load container and a maximum displacement of 0.136 respectively for the two loading conditions. These maximum displacements, which can be negligible shows that the load container is well designed as the maximum stresses are above the hand calculated maximum stresses.

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Steady and dynamic states analysis of induction motor: FEA approach

Cited by 10 publications

References 11 publications

Design and Fault Diagnosis of Induction Motor Using ML-Based Algorithms for EV Application

Design and Fault Diagnosis of Induction Motor Using ML-Based Algorithms for EV Application

Comparison and assessment of a different steel materials based on core losses reduction for three-phase induction motor

Static fatigue analysis on the load container of a low-cost vehicle for Nigerian rural farmers

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