Reducing induction motor identified parameters using a nonlinear Lasso method

M. Rasouli; D. T. Westwick; W. D. Rosehart

doi:10.1016/j.epsr.2012.01.011

Reducing induction motor identified parameters using a nonlinear Lasso method

M. Rasouli, D. T. Westwick, W. D. Rosehart

School of Engineering (Behrend)

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Reliable induction motor modeling is critical in power system planning and operation. This paper considers the identifiability of induction motor parameters, with a particular emphasis placed on using subset selection and shrinkage methods to allow the identification methods to focus on the most significant parameters. The proposed approach is validated using experimental data and the results found are compared to those of a recently proposed method based on sensitivity analysis.

Original language	English (US)
Pages (from-to)	1-8
Number of pages	8
Journal	Electric Power Systems Research
Volume	88
DOIs	https://doi.org/10.1016/j.epsr.2012.01.011
State	Published - Jul 2012

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1016/j.epsr.2012.01.011

Cite this

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title = "Reducing induction motor identified parameters using a nonlinear Lasso method",

abstract = "Reliable induction motor modeling is critical in power system planning and operation. This paper considers the identifiability of induction motor parameters, with a particular emphasis placed on using subset selection and shrinkage methods to allow the identification methods to focus on the most significant parameters. The proposed approach is validated using experimental data and the results found are compared to those of a recently proposed method based on sensitivity analysis.",

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AU - Westwick, D. T.

AU - Rosehart, W. D.

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N2 - Reliable induction motor modeling is critical in power system planning and operation. This paper considers the identifiability of induction motor parameters, with a particular emphasis placed on using subset selection and shrinkage methods to allow the identification methods to focus on the most significant parameters. The proposed approach is validated using experimental data and the results found are compared to those of a recently proposed method based on sensitivity analysis.

AB - Reliable induction motor modeling is critical in power system planning and operation. This paper considers the identifiability of induction motor parameters, with a particular emphasis placed on using subset selection and shrinkage methods to allow the identification methods to focus on the most significant parameters. The proposed approach is validated using experimental data and the results found are compared to those of a recently proposed method based on sensitivity analysis.

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JO - Electric Power Systems Research

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Reducing induction motor identified parameters using a nonlinear Lasso method

Abstract

All Science Journal Classification (ASJC) codes

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