ANN based RF-MRAS speed estimation of induction motor drive at low speed

Tiwari, Archana; Kumar, Bhavnesh; Chauhan, Yogesh K.

doi:10.1109/iceca.2017.8212789

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…The ANN can be described as a set of neurons that are connected in and organized in several layers [23] as shown in Figure 4. In this paper a Feedforward Neural Network (FFNN) is applied for speed estimation the IM motor.…”

Section: Ann Sensorless Based Techniquementioning

confidence: 99%

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Intelligent control of induction motor without speed sensor

Elgohary

Gouda

Eskander

2021

IJPEDS

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<p>This paper presents a proposed sensorless algorithm for induction motor(IM)speed control based on artificial neural networks (ANNs).The Indirect rotor field oriented (IRFO) technique is applied to control the motor. It is designed based on the proportional-integral (PI) controller. The particle swarm optimization (PSO) algorithm is used as a good solution for the problems associated with the design of the proportional-integral (PI) controller gains.The PSO is compared with the conventional methods. The proposed controller (PSO-PI) is then integrated with the artificial neural network(ANN) speed estimator. The MATLAB/Simulink is used for the simulation of the system. The obtained simulation results for the proposed technique are very close to the actual ones.</p>

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Section: Ann Sensorless Based Techniquementioning

confidence: 99%

“…The FL was employed with MRAS in [21]. The ANN technique used with MRAS in [23]. The results were good but still depend on the motor parameters and lost the basic advantage of the AI techniques which make them more adequate to deal with the system with parameters independently.…”

Section: Introductionmentioning

confidence: 99%

Intelligent control of induction motor without speed sensor

Elgohary

Gouda

Eskander

2021

IJPEDS

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“…The replacement of the adaptive model of conventional RF-MARS by neural networks in speed estimation of IMD reduces the computational efforts and is also immune to the effect of stator and rotor resistance variation on the system. A great improvement in the performance of the speed estimator is achieved, particularly at low speeds, both with open-loop and closed-loop conditions [43,44]. The online rotor and stator resistance estimation approaches are proposed, where a fixed learning rate is adopted during the estimation approach [45,46].…”

Section: Introductionmentioning

confidence: 99%

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

Kanakabettu,

Irvathoor,

Saralaya

et al. 2024

Energies

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This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor’s speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive.

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“…In addition, the introduction of this fragile device results in a decrease in the reliability of the system which requires special care for itself. has become a serious subject of research study in recent years [16]− [18]. This work will be devoted to the implementation of an algorithm for observing the speed of a DFIM using the sliding mode observer.…”

Section: Introductionmentioning

confidence: 99%

A new approach of robust speed-sensorless control of doubly fed induction motor fed by photovoltaic solar panel

Mounira

Cherifi

2023

IJPEDS

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<span lang="EN-US">In this work, we presented the modelling and simulation of the electrical operation of a photovoltaic (PV) system adapted by an MPPT control, the latter is applied to the robust observer for the control of the speed without sensor of a double fed induction motor (DFIM).Our machine is powered by the PV system, where the chosen control is the direct field oriented control with sliding mode speed regulator is used for the control of this machine which is powered by two pulse-width modulation (PWM) voltage inverters, finally the speed estimation of induction motor with observer based dual feed in sliding mode is presented. The simulation results show the efficiency of the proposed method.</span>

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ANN based RF-MRAS speed estimation of induction motor drive at low speed

Cited by 7 publications

References 9 publications

Intelligent control of induction motor without speed sensor

Intelligent control of induction motor without speed sensor

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

A new approach of robust speed-sensorless control of doubly fed induction motor fed by photovoltaic solar panel

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