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

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  • Ajithanjaya Kumar Mijar Kanakabettu

    (Department of Electrical and Electronics Engineering, St. Joseph Engineering College, Mangalore 575028, India
    Department of Electrical and Electronics, Nitte Meenakshi Institute of Technology, Bengaluru 560064, India)

  • Rajkiran Ballal Irvathoor

    (Department of Electrical and Electronics Engineering, Channabasaveshwara Institute of Technology, Tumkur 572216, India)

  • Sanath Saralaya

    (Department of Electrical and Electronics Engineering, St. Joseph Engineering College, Mangalore 575028, India)

  • Sathyendra Bhat Jodumutt

    (Department of Computer Applications, St. Joseph Engineering College, Mangalore 575028, India)

  • Athokpam Bikramjit Singh

    (Department of Computer Science and Engineering, Yenepoya Institute of Technology, Mangalore 574225, India)

Abstract

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.

Suggested Citation

  • Ajithanjaya Kumar Mijar Kanakabettu & Rajkiran Ballal Irvathoor & Sanath Saralaya & Sathyendra Bhat Jodumutt & Athokpam Bikramjit Singh, 2024. "Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives," Energies, MDPI, vol. 17(9), pages 1-30, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2150-:d:1386897
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    References listed on IDEAS

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    1. Plamena Dinolova & Vyara Ruseva & Ognyan Dinolov, 2023. "Energy Efficiency of Induction Motor Drives: State of the Art, Analysis and Recommendations," Energies, MDPI, vol. 16(20), pages 1-26, October.
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