ANN based Flux Estimator for Rotor Resistance Estimation in Vector Controlled IM Drives

Venkadesan, A.; Himavathi, S.; Muthuramalingam, A.; Sedhuraman, K.

doi:10.1109/icscan.2018.8541240

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…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]. The selection of an inappropriate learning rate leads to output errors with slow convergence of the error during network training.…”

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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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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“…Other articles [23,24] propose an online estimated rotor resistance method using a neural network. However, the proposed method is still limited by the learning rate that is pre-selected and does not change during the rotor resistance estimation process.…”

mentioning

confidence: 99%

Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive

et al. 2020

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This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

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Induction motor drive with field-oriented control and speed estimation using feedforward neural network

Baca

Kouril

Palacký

et al. 2020

2020 21st International Scientific Conference on Electric Power Engineering (EPE)

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ANN based Flux Estimator for Rotor Resistance Estimation in Vector Controlled IM Drives

Cited by 4 publications

References 9 publications

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

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

Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive

Induction motor drive with field-oriented control and speed estimation using feedforward neural network

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