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

• 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
• ISSN: 2504-3110; 2504-3110
• DOI: 10.3390/fractalfract7060485

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.