Reducing induction motor identified parameters using a nonlinear Lasso method

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

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalElectric Power Systems Research
StatePublished - Jul 2012

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Reducing induction motor identified parameters using a nonlinear Lasso method'. Together they form a unique fingerprint.

Cite this