TY - GEN
T1 - An expert model of switched reluctance motor using decision tree learning algorithms
AU - Dehkordi, Behzad Mirzaeian
AU - Zafarani, Reza
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Decision tree learning algorithms
KW - Modeling
KW - Reduced error pruning algorithm
KW - SR motor
UR - http://www.scopus.com/inward/record.url?scp=51249112224&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51249112224&partnerID=8YFLogxK
U2 - 10.1109/ACEMP.2007.4510571
DO - 10.1109/ACEMP.2007.4510571
M3 - Conference contribution
AN - SCOPUS:51249112224
SN - 1424408911
SN - 9781424408917
T3 - International Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference
SP - 267
EP - 272
BT - International Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference
T2 - International Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP'07 and Electromotion'07 Joint Conference
Y2 - 10 September 2007 through 12 September 2007
ER -