Simple and Efficient Direct Torque Control of Induction Motor Based on Artificial Neural Networks

Bouhoune, Kenza; Yazid, Krim; Boucherit, Mohamed Seghir; Nahid‐Mobarakeh, Babak

doi:10.1109/esars-itec.2018.8607747

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Strategy. Inspired typically by the highly evolved functionality of the human brain, ANNs have invaded the feld of advanced electrical drives considerably [23]. With the capacity to learn from already existing systems, this intelligent technique is able to regenerate new generalized models that can adapt to situations that have not been considered during the learning phase [27].…”

Section: Design Of Neural Decision Table For the Smart Dtcmentioning

confidence: 99%

An Improved Direct Torque Control with an Advanced Broken-Bar Fault Diagnosis for Induction Motor Drives

Aissa,

Reffas,

Talhaoui

et al. 2023

International Transactions on Electrical Energy Systems

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This paper presents an advanced strategy combining fuzzy logic and artificial neural networks (ANNs) for direct torque control (DTC) and broken-bar fault diagnosis in induction motors. More specifically, a fuzzy-based controller is used to simultaneously minimize the stator flux and the electromagnetic torque ripples. A neural switching table is then proposed to achieve the interface inverter control. Besides, a closed-loop broken-bar fault detection strategy based on the Hilbert technique (HT) with the discrete wavelet transform (DWT) and ANNs is proposed. The fault detection is performed by analyzing the induction motor’s stator current by using the combined techniques HT-DWT. The effect of a broken-bar fault on the machine varies according to the number and position of the broken bars. The neural detector was used in order to identify the number of broken bars through only one current measurement. The effectiveness of the developed control has been verified using MATLAB/Simulink and real-time simulation in OPAL-RT 4510. Obtained results show improved performances in terms of torque ripple minimization and stator current quality, evaluated, respectively, at 43.75% and 41.26% as well as a rigorous motor health monitoring.

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Section: Design Of Neural Decision Table For the Smart Dtcmentioning

confidence: 99%

An Improved Direct Torque Control with an Advanced Broken-Bar Fault Diagnosis for Induction Motor Drives

Aissa,

Reffas,

Talhaoui

et al. 2023

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…MLP is a multi-layered perceptron and learns by using a hidden layer addition and a back-propagation algorithm between the input layer and the output layer to enable nonlinear classification that the single-layer perceptron cannot solve [29]. [30] proposes an artificial neural network (ANN) controller to reduce both torque and flux ripples by considering appropriate input and feedback values through online mode. By using both simulation and process, it shows that the proposed controller is more efficient than the traditional controller.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

2021

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This article presents an artificial neural network (ANN) for loss minimization of direct torque-controlled induction motor (IM)-driven electric vehicles. The IM drive can consume more power than it needs to be, especially when it is operating under conditions under full load. The proposed architecture and its control strategies use ANN to control the amplitude starting current and save more power. The performance of these controllers was verified by simulation using the MATLAB/SIMULINK package, and the results showed good and high performance in the time domain response and rapid rejection of the system disturbance compared to the conventional proportional-integral derivative (PID) controller. Thus, the core loss of IM greatly reduces, thus improving the efficiency of the driving system. Finally, the experimental results were validated which were highly consistent with the simulation results using the DSPACE MicroLabBox controller.

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DTC of Matrix Converter Fed Induction Machine Based on Fuzzy Logic

Swami

Kumar

2020

Recent Advances in Power Electronics and Drives

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Simple and Efficient Direct Torque Control of Induction Motor Based on Artificial Neural Networks

Cited by 7 publications

References 17 publications

An Improved Direct Torque Control with an Advanced Broken-Bar Fault Diagnosis for Induction Motor Drives

An Improved Direct Torque Control with an Advanced Broken-Bar Fault Diagnosis for Induction Motor Drives

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

DTC of Matrix Converter Fed Induction Machine Based on Fuzzy Logic

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