An expert model of switched reluctance motor using decision tree learning algorithms

Behzad Mirzaeian Dehkordi, Reza Zafarani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper two different Decision Tree learning systems for modeling of a switched reluctance motor have been developed. The design vector consists of the design parameters in the first one whereas in the second one, it is a combination of hysteresis current band in the current limiter and the switching angles. The output performance variables are efficiency and torque ripple in both systems. An accurate analysis program based on Improved Magnetic Equivalent Circuit (IMEC) method has been used to generate the input-output data. These input-output data is used to produce the Decision Trees for predicting the performance of Switched Reluctance Motor (SRM). The performance prediction results for a 6/8, 4kw, SR motor show good agreement with the results obtained from IMEC method or Finite Element (FE) analysis. The developed Decision Tree systems can be used for fast prediction of motor performance in the optimal design process or on-line control schemes of SR motor.

Original languageEnglish (US)
Title of host publicationInternational Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference
Pages267-272
Number of pages6
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
EventInternational Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference - Bodrum, Turkey
Duration: Sep 10 2007Sep 12 2007

Publication series

NameInternational Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference

Other

OtherInternational Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference
CountryTurkey
CityBodrum
Period9/10/079/12/07

Keywords

  • Decision tree learning algorithms
  • Modeling
  • Reduced error pruning algorithm
  • SR motor

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Communication

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  • Cite this

    Dehkordi, B. M., & Zafarani, R. (2007). An expert model of switched reluctance motor using decision tree learning algorithms. In International Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference (pp. 267-272). [4510571] (International Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference). https://doi.org/10.1109/ACEMP.2007.4510571