Stator q-axis voltage error based sensorless speed estimation of field oriented vector controlled induction motor without using voltage transducer

Abstract This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.

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A single stage smoothing filter for the speed estimation of three phase induction motor

2016 IEEE 7th Power India International Conference (PIICON) ◽

10.1109/poweri.2016.8077353 ◽

2016 ◽

Author(s):

Uma Syamkumar ◽

B. Jayanand

Keyword(s):

Induction Motor ◽

Single Stage ◽

Speed Estimation ◽

Smoothing Filter ◽

Three Phase

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A Differential-Algebraic Approach to Speed Estimation in an Induction Motor

IEEE Transactions on Automatic Control ◽

10.1109/tac.2006.878775 ◽

2006 ◽

Vol 51 (7) ◽

pp. 1172-1177 ◽

Cited By ~ 15

Author(s):

M. Li ◽

J. Chiasson ◽

M. Bodson ◽

L.M. Tolbert

Keyword(s):

Induction Motor ◽

Algebraic Approach ◽

Speed Estimation

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Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i5.pp2691-2702 ◽

2018 ◽

Vol 8 (5) ◽

pp. 2691

Author(s):

Mini R ◽

Shabana Backer P. ◽

B. Hariram Satheesh ◽

Dinesh M. N

Keyword(s):

Comparative Analysis ◽

Induction Motor ◽

Reactive Power ◽

Adaptive Controller ◽

Operating Conditions ◽

Estimation Methods ◽

Speed Estimation ◽

Loop Model ◽

Low Speed ◽

Rotor Flux

<p>This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.</p>

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