Identification of Fractional Models of an Induction Motor with Errors in Variables

The skin effect in modeling an induction motor can be described by fractional differential equations. The existing methods for identifying the parameters of an induction motor with a rotor skin effect suggest the presence of errors only in the output. The presence of errors in measuring currents and...

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Bibliographic Details
Published in:Fractal and fractional 2023-06, Vol.7 (6), p.485
Main Author: Ivanov, Dmitriy
Format: Article
Language:English
Subjects:
Accuracy
additive noise
Circuits
Conversion
Diagnostic systems
Differential equations
Errors
errors-in-variables
Estimates
Fault diagnosis
Fractional calculus
fractional derivative
Induction electric motors
induction motor
Induction motors
Least squares method
Mathematical models
Noise
Parameter estimation
Parameter identification
Skin effect
total least squares
Variables
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Summary:The skin effect in modeling an induction motor can be described by fractional differential equations. The existing methods for identifying the parameters of an induction motor with a rotor skin effect suggest the presence of errors only in the output. The presence of errors in measuring currents and voltages leads to errors in both input and output signals. Applying standard methods, such as the ordinary least squares method, leads to biased estimates in these types of problems. The study proposes a new method for identifying the parameters of an induction motor in the presence of a skin effect. Estimates of parameters were determined based on generalized total least squares. The simulation results obtained showed the high accuracy of the obtained estimates. The results of this research can be applied in the development of predictive diagnostic systems. This study shows that ordinary least squares parameter estimates can lead to incorrect operation of the fault diagnosis system.
ISSN:2504-3110
2504-3110
DOI:10.3390/fractalfract7060485