Generalized total least squares for identification of electromagnetic parameters of an induction motor

Иванов, Д. В.; Sandler, Ilya L.; Chertykovtseva, N. V.

doi:10.1088/1742-6596/2032/1/012093

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…Estimates of K parameters obtained by formulas ( 14) and (15) were compared with OLS estimates of K parameters. The parameters of the induction motor are taken from the paper [7]: nominal rated power .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…proposed in [2], [3], [4], [5]. In [ 7], the algorithm for identifying K parameters in the presence of errors in measuring currents and voltages based on generalised total least squares is developed.…”

mentioning

confidence: 99%

“…In [7], it was proposed to use generalized total least squares ( 9) to estimate the parameters of an induction motor. For the objective function ( 12), this approach cannot be applied due to the singularity of the matrix H .…”

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confidence: 99%

“…Then the estimates of the parameters of the asynchronous electric motor can be calculated from the equations The solution to the biased normal system (13) can be represented as an augmented equivalent system [7], [9] ( )…”

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confidence: 99%

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Identification of parameters of induction motor with error of speed sensor

Иванов

Sandler

Mitroshin

et al. 2022

J. Phys.: Conf. Ser.

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This paper proposes a new approach to parametric identification of induction motor with error of speed sensor. The ordinary least squares (OLS) estimates are shown to be biased. Generalized total least squares (GTLS) with a singular covariance matrix of the error are propose for estimation of K parameters of induction motor. Identification of parameters takes place in 2 steps. At the first step, two K parameters are estimated using GTLS with a singular error matrix. At the second step, the remaining three K parameters are estimated. An equivalent augmented system is used to solve a biased normal system. Based on the simulation results, a comparison of the accuracy of the estimates of the proposed algorithm and ordinary least squares estimates is given.

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Section: Simulation Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Identification of parameters of induction motor with error of speed sensor

Иванов

Sandler

Mitroshin

et al. 2022

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Estimation of induction motor parameters based on ordinary least squares (OLS) and its recursive modifications is considered in [3][4][5][6]. The application of the total least squares (TLS) technique and its recursive versions was proposed in articles [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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

Иванов

2023

Fractal Fract

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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.

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Estimation of Vehicle Status and Parameters Based on Nonlinear Kalman Filtering

Huang

2022

2022 6th International Conference on Robotics and Automation Sciences (ICRAS)

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Generalized total least squares for identification of electromagnetic parameters of an induction motor

Cited by 4 publications

References 3 publications

Identification of parameters of induction motor with error of speed sensor

Identification of parameters of induction motor with error of speed sensor

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

Estimation of Vehicle Status and Parameters Based on Nonlinear Kalman Filtering

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