NDLI: Stator current model reference adaptive systems speed estimator for regenerating-mode low-speed operation of sensorless induction motor drives

Please wait, while we are loading the content...

Biosurface and Biotribology

CAAI Transactions on Intelligence Technology

CIRED - Open Access Proceedings Journal

Chinese Journal of Electronics

Cognitive Computation and Systems

Electronics Letters

Energy Conversion and Economics

Engineering & Technology

Engineering Biology

Healthcare Technology Letters

IET Circuits, Devices & Systems

IET Collaborative Intelligent Manufacturing

IET Communications

IET Computer Vision

IET Computers & Digital Techniques

IET Control Theory & Applications

IET Cyber-Physical Systems: Theory & Applications

IET Cyber-Systems and Robotics

IET Electric Power Applications

Volume 15 (2021)

Volume 14 (2020)

Volume 13 (2019)

Volume 12 (2018)

Volume 11 (2017)

Volume 10 (2016)

Volume 9 (2015)

Volume 8 (2014)

Volume 7 (2013)

Volume 7 (2013) : Issue 9, Nov

Volume 7 (2013) : Issue 8, Sep

Volume 7 (2013) : Issue 7, Aug

Analytical modelling and parametric sensitivity analysis for the PMSM steady-state performance prediction

Anisotropic properties of non-oriented steel sheets

Characterising brushless doubly fed machine rotors

Correction of saturated current from measurement current transformer

Dynamic modelling of the brushless doubly fed machine

Rim-drive marine thruster using a multiple-can induction motor

Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives

Sensorless position estimation of switched reluctance motor at startup using quadratic polynomial regression

Stator current model reference adaptive systems speed estimator for regenerating-mode low-speed operation of sensorless induction motor drives

Three effective vectors-based current control scheme for four-switch three-phase trapezoidal brushless DC motor

Volume 7 (2013) : Issue 6, Jul

Volume 7 (2013) : Issue 5, May

Volume 7 (2013) : Issue 4, Apr

Volume 7 (2013) : Issue 3, Mar

Volume 7 (2013) : Issue 2, Feb

Volume 7 (2013) : Issue 1, Jan

Volume 5 (2011)

Volume 4 (2010)

IET Electrical Systems in Transportation

IET Energy Systems Integration

IET Generation, Transmission & Distribution

IET Image Processing

IET Information Security

IET Intelligent Transport Systems

IET Microwaves, Antennas & Propagation

IET Nanobiotechnology

IET Nanodielectrics

IET Optoelectronics

IET Power Electronics

IET Quantum Communication

IET Radar, Sonar & Navigation

IET Renewable Power Generation

IET Science, Measurement & Technology

IET Signal Processing

IET Smart Cities

IET Systems Biology

IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Stator current model reference adaptive systems speed estimator for regenerating-mode low-speed operation of sensorless induction motor drives

Content Provider	IET Digital Library
Author	Gadoue, Shady M. Giaouris, Damian Finch, John W.
Abstract	The performance of a stator current-based model reference adaptive systems (MRAS) speed estimator for sensorless induction motor drives is investigated in this study. The measured stator currents are used as a reference model for the MRAS observer to avoid the use of a pure integrator. A two-layer, online-trained neural network stator current observer is used as the adaptive model for the MRAS estimator which requires the rotor flux information. This can be obtained from the voltage or current models, but instability and dc drift can downgrade the overall observer performance. To overcome these problems of rotor flux estimation, an off-line trained multilayer feed-forward neural network is proposed here as a rotor flux observer. Hence, two networks are employed: the first is online trained for stator current estimation and the second is off-line trained for rotor flux estimation. Sensorless operation for the proposed MRAS scheme using current model and neural network rotor flux observers are investigated based on a set of experimental tests in the low-speed region. Using a neural network rotor flux observer to replace the current model is shown to solve the stability problem in the low-speed regenerating mode of operation.
Starting Page	597
Ending Page	606
Page Count	10
ISSN	17518660
Volume Number	7
e-ISSN	17518679
Issue Number	Issue 7, Aug (2013)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-epa/7/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-epa.2013.0091
Journal	IET Electric Power Applications
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acceleration Angular Velocity Control Asynchronous Machine Control of Electric Power System Current Control Current Measurement Drive Electric Current Control Electric Current Measurement Feedforward Neural Network Induction Motor Drive Learning in AI Model Reference Adaptive Control System MRAS Neurocontrol Neurocontrollers Observers Offline Trained Multilayer Feedforward Neural Network Regenerating-mode Low-speed Operation Rotation Control Rotor Flux Estimation Information Rotor Flux Observer Sensorless Induction Motor Drive Sensorless Machine Control Speed Estimation Stability Stability in Control Theory Stability Problem Stator Current Estimation Stator Current Measurement Stator Current Model Reference Adaptive System Stators Two-layer Online-trained Neural Network Stator Current Observer Velocity Voltage Control
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in