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

10 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
Publication status	Published - Jul 1 2012

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All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Electrical and Electronic Engineering

Cite this

Rasouli, M., Westwick, D. T., & Rosehart, W. D. (2012). Reducing induction motor identified parameters using a nonlinear Lasso method. Electric Power Systems Research, 88, 1-8. https://doi.org/10.1016/j.epsr.2012.01.011

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Access to Document

10.1016/j.epsr.2012.01.011