DOE Patents title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Method and system for determining induction motor speed

Abstract

A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.

Inventors:
;
Issue Date:
Research Org.:
Texas A And M University, College Station, TX (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1174794
Patent Number(s):
6713978
Application Number:
10/197,881
Assignee:
Texas A And M University (College Station, TX)
Patent Classifications (CPCs):
H - ELECTRICITY H02 - GENERATION H02P - CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS
DOE Contract Number:  
FG07-98ID13641
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Parlos, Alexander G., and Bharadwaj, Raj M. Method and system for determining induction motor speed. United States: N. p., 2004. Web.
Parlos, Alexander G., & Bharadwaj, Raj M. Method and system for determining induction motor speed. United States.
Parlos, Alexander G., and Bharadwaj, Raj M. Tue . "Method and system for determining induction motor speed". United States. https://www.osti.gov/servlets/purl/1174794.
@article{osti_1174794,
title = {Method and system for determining induction motor speed},
author = {Parlos, Alexander G. and Bharadwaj, Raj M.},
abstractNote = {A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Mar 30 00:00:00 EST 2004},
month = {Tue Mar 30 00:00:00 EST 2004}
}

Works referenced in this record:

A speed identifier for induction motor drives using real-time adaptive digital filtering
journal, January 1998


Recent developments of induction motor drives fault diagnosis using AI techniques
journal, January 2000


Sensorless speed measurement using current harmonic spectral estimation in induction machine drives
journal, January 1996


Effects of time-varying loads on rotor fault detection in induction machines
journal, January 1995


Rotor-speed estimator for induction motors using voltage and current measurements
journal, March 1998


An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current
journal, January 1999


Adaptive speed identification for vector control of induction motors without rotational transducers
journal, January 1992


Performance of FFT-rotor slot harmonic speed detector for sensorless induction motor drives
journal, January 1996


Design, DSP implementation, and performance of artificial-intelligence-based speed estimators for electromechanical drives
journal, March 1998


Induction motors' faults detection and localization using stator current advanced signal processing techniques
journal, January 1999