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

Ajithanjaya Kumar Mijar Kanakabettu (), Rajkiran Ballal Irvathoor, Sanath Saralaya, Sathyendra Bhat Jodumutt and Athokpam Bikramjit Singh
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Ajithanjaya Kumar Mijar Kanakabettu: Department of Electrical and Electronics Engineering, St. Joseph Engineering College, Mangalore 575028, 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

Energies, 2024, vol. 17, issue 9, 1-30

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.

Keywords: artificial neural network; speed sensorless vector-controlled drives; adaptive learning rate; stator resistance; rotor resistance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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